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1
Tsochantaridis, Ioannis. "Support vector machine learning for interdependent and structured output spaces /". View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174684.
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Zhang, Shi-Xiong. "Structured support vector machines for speech recognition". Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708040.
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Sharma, Siddharth. "Application of Support Vector Machines for Damage Detection in Structures". Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/8.
Testo completoAbstract (sommario):
Support vector machines (SVMs) are a set of supervised learning methods that have recently been applied for structural damage detection due to their ability to form an accurate boundary from a small amount of training data. During training, they require data from the undamaged and damaged structure. The unavailability of data from the damaged structure is a major challenge in such methods due to the irreversibility of damage. Recent methods create data for the damaged structure from finite element models. In this thesis we propose a new method to derive the dataset representing the damage structure from the dataset measured on the undamaged structure without using a detailed structural finite element model. The basic idea is to reduce the values of a copy of the data from the undamaged structure to create the data representing the damaged structure. The performance of the method in the presence of measurement noise, ambient base excitation, wind loading is investigated. We find that SVMs can be used to detect small amounts of damage in the structure in the presence of noise. The ability of the method to detect damage at different locations in a structure and the effect of measurement location on the sensitivity of the method has been investigated. An online structural health monitoring method has also been proposed to use the SVM boundary, trained on data measured from the damaged structure, as an indicator of the structural health condition.
4
Zhong, Wei. "Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction". Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/7.
Testo completoAbstract (sommario):
Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied.
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Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.
Testo completoAbstract (sommario):
Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.
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Guimarães, Ana Paula Alves [UNESP]. "Utilização do algoritmo de aprendizado de máquinas para monitoramento de falhas em estruturas inteligentes". Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148718.
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O monitoramento da condição estrutural é uma área que vem sendo bastante estudada por permitir a construção de sistemas que possuem a capacidade de identificar um determinado dano em seu estágio inicial, podendo assim evitar sérios prejuízos futuros. O ideal seria que estes sistemas tivessem o mínimo de interferência humana. Sistemas que abordam o conceito de aprendizagem têm a capacidade de serem autômatos. Acredita-se que por possuírem estas propriedades, os algoritmos de aprendizagem de máquina sejam uma excelente opção para realizar as etapas de identificação, localização e avaliação de um dano, com capacidade de obter resultados extremamente precisos e com taxas mínimas de erros. Este trabalho tem como foco principal utilizar o algoritmo support vector machine no auxílio do monitoramento da condição de estruturas e, com isto, obter melhor exatidão na identificação da presença ou ausência do dano, diminuindo as taxas de erros através das abordagens da aprendizagem de máquina, possibilitando, assim, um monitoramento inteligente e eficiente. Foi utilizada a biblioteca LibSVM para análise e validação da proposta. Desta forma, foi possível realizar o treinamento e classificação dos dados promovendo a identificação dos danos e posteriormente, empregando as predições efetuadas pelo algoritmo, foi possível determinar a localização dos danos na estrutura. Os resultados de identificação e localização dos danos foram bastante satisfatórios.
Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.
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Guimarães, Ana Paula Alves. "Utilização do algoritmo de aprendizado de máquinas para monitoramento de falhas em estruturas inteligentes /". Ilha Solteira, 2016. http://hdl.handle.net/11449/148718.
Testo completoAbstract (sommario):
Orientador: Vicente Lopes JuniorResumo: Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.
Mestre
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Dalvi, Aditi. "Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791.
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Altun, Gulsah. "Machine Learning and Graph Theory Approaches for Classification and Prediction of Protein Structure". Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/cs_diss/31.
Testo completoAbstract (sommario):
Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.
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Uziela, Karolis. "Protein Model Quality Assessment : A Machine Learning Approach". Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-137695.
Testo completoAbstract (sommario):
Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). These programs are able to predict model accuracy before the native structure is determined. The accuracy estimation can be divided into two parts: global (the whole model accuracy) and local (the accuracy of each residue). ProQ2 is one of the most successful MQAPs for prediction of both local and global model accuracy and is based on a Machine Learning approach. In this thesis, I present my own contribution to Model Quality Assessment (MQA) and the newest developments of ProQ program series. Firstly, I describe a new ProQ2 implementation in the protein modelling software package Rosetta. This new implementation allows use of ProQ2 as a scoring function for conformational sampling inside Rosetta, which was not possible before. Moreover, I present two new methods, ProQ3 and ProQ3D that both outperform their predecessor. ProQ3 introduces new training features that are calculated from Rosetta energy functions and ProQ3D introduces a new machine learning approach based on deep learning. ProQ3 program participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) and was one of the best methods in the MQA category. Finally, an important issue in model quality assessment is how to select a target function that the predictor is trying to learn. In the fourth manuscript, I show that MQA results can be improved by selecting a contact-based target function instead of more conventional superposition based functions.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.
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Hu, Hae-Jin. "Design of Comprehensible Learning Machine Systems for Protein Structure Prediction". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/22.
Testo completoAbstract (sommario):
With the efforts to understand the protein structure, many computational approaches have been made recently. Among them, the Support Vector Machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model; the predictions made by SVM cannot be interpreted as biologically meaningful way. To overcome this limitation, a new association rule based classifier PCPAR was devised based on the existing classifier, CPAR to handle the sequential data. The performance of the PCPAR was improved more by designing the following two hybrid schemes. The PCPAR/SVM method is a parallel combination of the PCPAR and the SVM and the PCPAR_SVM method is a sequential combination of the PCPAR and the SVM. To understand the SVM prediction, the SVM_PCPAR scheme was developed. The experimental result presents that the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method. The PCPAR/SVM scheme presents better performance than the PCPAR, PCPAR_SVM or the SVM_PCPAR and almost equal performance to the SVM. The generated patterns are easily understandable and biologically meaningful. The system sturdiness evaluation and the ROC curve analysis proved that this new scheme is robust and competent.
12
Chida, Anjum A. "Protein Tertiary Model Assessment Using Granular Machine Learning Techniques". Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/65.
Testo completoAbstract (sommario):
The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same class as the PDB structures (correct or incorrect protein models). Different subsets of PDB (protein data bank) are considered for evaluating the prediction potential of the machine learning methods. Here we show two such machines, one using SVM (support vector machines) and another using fuzzy decision trees (FDT). First using a preliminary encoding style SVM could get around 70% in protein model quality assessment accuracy, and improved Fuzzy Decision Tree (IFDT) could reach above 80% accuracy. For the purpose of reducing computational overhead multiprocessor environment and basic feature selection method is used in machine learning algorithm using SVM.
Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment.
Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same.
13
Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks". Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.
Testo completoAbstract (sommario):
Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to efficiently identify a network's set of attractors and to predict the likelihood with which an arbitrary state leads to each of the network’s attractors. These probabilities will be represented using a fuzzy membership vector.
Predicting fuzzy membership vectors using machine learning techniques may address the intractability posed by networks of realistic size and complexity. Modeling and simulation can be used to provide the necessary training sets for machine learning methods to predict fuzzy membership vectors. The experiments comprise several GRNs, each represented by a set of output classes. These classes consist of thresholds τ and ¬τ, where τ = [τlaw,τhigh]; state s belongs to class τ if the probability of its transitioning to attractor belongs to the range [τlaw,τhigh]; otherwise it belongs to class ¬τ. Finally, each machine learning classifier was trained with the training sets that was previously collected. The objective is to explore methods to discover patterns for meaningful classification of states in realistically complex regulatory networks.
The research design took a GRN and a machine learning method as input and produced output class < Ατ > and its negation ¬ < Ατ >. For each GRN, attractors were identified, data was collected by sampling each state to create fuzzy membership vectors, and machine learning methods were trained to predict whether a state is in a healthy attractor or not. For T-LGL, SVMs had the highest accuracy in predictions (between 93.6% and 96.9%) and precision (between 94.59% and 97.87%). However, naive Bayesian classifiers had the highest recall (between 94.71% and 97.78%). This study showed that all experiments have extreme significance with pvalue < 0.0001. The contribution this research offers helps clinical biologist to submit genetic states to get an initial result on their outcomes. For future work, this implementation could use other machine learning classifiers such as xgboost or deep learning methods. Other suggestions offered are developing methods that improves the performance of state transition that allow for larger training sets to be sampled.
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Islam, Md Nasrul. "A Balanced Secondary Structure Predictor". ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/1995.
Testo completoAbstract (sommario):
Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algorithm (GA) with a support vector machine. We prepared two test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. Overall accuracy of our predictor was 76.4% and 77.2% respectively on CB471 and N295 datasets, while SPINE X gave 76.5% overall accuracy on both test datasets.
15
Kinalwa-Nalule, Myra. "Using machine learning to determine fold class and secondary structure content from Raman optical activity and Raman vibrational spectroscopy". Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/using-machine-learning-to-determine-fold-class-and-secondary-structure-content-from-raman-optical-activity-and-raman-vibrational-spectroscopy(7382043d-748c-4d29-ba75-67fb35ccdb19).html.
Testo completoAbstract (sommario):
The objective of this project was to apply machine learning methods to determine protein secondary structure content and protein fold class from ROA and Raman vibrational spectral data. Raman and ROA are sensitive to biomolecular structure with the bands of each spectra corresponding to structural elements in proteins and when combined give a fingerprint of the protein. However, there are many bands of which little is known. There is a need, therefore, to find ways of extrapolating information from spectral bands and investigate which regions of the spectra contain the most useful structural information. Support Vector Machines (SVM) classification and Random Forests (RF) trees classification were used to mine protein fold class information and Partial Least Squares (PLS) regression was used to determine secondary structure content of proteins. The classification methods were used to group proteins into α-helix, β-sheet, α/β and disordered fold classes. The PLS regression was used to determine percentage protein structural content from Raman and ROA spectral data. The analyses were performed on spectral bin widths of 10cm-1 and on the spectral amide regions I, II and III. The full spectra and different combinations of the amide regions were also analysed. The SVM analyses, classification and regression, generally did not perform well. SVM classification models for example, had low Matthew Correlation Coefficient (MCC) values below 0.5 but this is better than a negative value which would indicate a random chance prediction. The SVM regression analyses also showed very poor performances with average R2 values below 0.5. R2 is the Pearson's correlations coefficient and shows how well predicted and observed structural content values correlate. An R2 value 1 indicates a good correlation and therefore a good prediction model. The Partial Least Squares regression analyses yielded much improved results with very high accuracies. Analyses of full spectrum and the spectral amide regions produced high R2 values of 0.8-0.9 for both ROA and Raman spectral data. This high accuracy was also seen in the analysis of the 850-1100 cm-1 backbone region for both ROA and Raman spectra which indicates that this region could have an important contribution to protein structure analysis. 2nd derivative Raman spectra PLS regression analysis showed very improved performance with high accuracy R2 values of 0.81-0.97. The Random Forest algorithm used here for classification showed good performance. The 2-dimensional plots used to visualise the classification clusters showed clear clusters in some analyses, for example tighter clustering was observed for amide I, amide I & III and amide I & II & III spectral regions than for amide II, amide III and amide II&III spectra analysis. The Random Forest algorithm also determines variable importance which showed spectral bins were crucial in the classification decisions. The ROA Random Forest analyses performed generally better than Raman Random Forest analyses. ROA Random Forest analyses showed 75% as the highest percentage of correctly classified proteins while Raman analyses reported 50% as the highest percentage. The analyses presented in this thesis have shown that Raman and ROA vibrational spectral contains information about protein secondary structure and these data can be extracted using mathematical methods such as the machine learning techniques presented here. The machine learning methods applied in this project were used to mine information about protein secondary structure and the work presented here demonstrated that these techniques are useful and could be powerful tools in the determination protein structure from spectral data.
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Arslan, Hilal. "Machine Learning Methods For Promoter Region Prediction". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613363/index.pdf.
Testo completoAbstract (sommario):
Promoter classification is the task of separating promoter from non promoter sequences. Determining promoter regions where the transcription initiation takes place is important for several reasons such as improving genome annotation and defining transcription start sites. In this study, various promoter prediction methods called ProK-means, ProSVM, and 3S1C are proposed. In ProSVM and ProK-means algorithms, structural features of DNA sequences are used to distinguish promoters from non promoters. Obtained results are compared with ProSOM which is an existing promoter prediction method. It is shown that ProSVM is able to achieve greater recall rate compared to ProSOM results. Another promoter prediction methods proposed in this study is 3S1C. The difference of the proposed technique from existing methods is using signal, similarity, structure, and context features of DNA sequences in an integrated way and a hierarchical manner. In addition to current methods related to promoter classification, the similarity feature, which compares the promoter regions between human and other species, is added to the proposed system. We show that the similarity feature improves the accuracy. To classify core promoter regions, firstly, signal, similarity, structure, and context features are extracted and then, these features are classified separately by using Support Vector Machines. Finally, output predictions are combined using multilayer perceptron. The result of 3S1C algorithm is very promising.
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Bhattacharjee, Puranjoy. "Correlation Between Computed Equilibrium Secondary Structure Free Energy and siRNA Efficiency". Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/34643.
Testo completoAbstract (sommario):
We have explored correlations between the measured efficiency of the RNAi process and
several computed signatures that characterize equilibrium secondary structure of the partic-
ipating mRNA, siRNA, and their complexes. A previously published data set of 609 exper-
imental points was used for the analysis. While virtually no correlation with the computed
structural signatures are observed for individual data points, several clear trends emerge
when the data is averaged over 10 bins of N â ¼ 60 data points per bin.
The strongest trend is a positive linear (r 2 = 0.87) correlation between ln(remaining mRNA)
and â Gms , the combined free energy cost of unraveling the siRNA and creating the break
in the mRNA secondary structure at the complementary target strand region. At the same
time, the free energy change â Gtotal of the entire process mRNA + siRNA â (mRNA â
siRNA)complex is not correlated with RNAi efficiency, even after averaging. These general
findings appear to be robust to details of the computational protocols. The correlation be-
tween computed â Gms and experimentally observed RNAi efficiency can be used to enhance
the ability of a machine learning algorithm based on a support vector machine (SVM) to
predict effective siRNA sequences for a given target mRNA. Specifically, we observe modest,
3 to 7%, but consistent improvement in the positive predictive value (PPV) when the SVM
training set is pre- or post-filtered according to a â Gms threshold.
Master of Science
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Bisognin, Gustavo. "Utilização de máquinas de suporte vetorial para predição de estruturas terciárias de proteínas". Universidade do Vale do Rio do Sinos, 2007. http://www.repositorio.jesuita.org.br/handle/UNISINOS/2233.
Testo completoAbstract (sommario):
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A estrutura tridimensional de uma proteína está diretamente ligada a sua função. Diversos projetos de seqüenciamento genéticos acumulam um grande número de seqüências de proteínas cujas estruturas primárias e secundárias são conhecidas. Entretanto, as informações sobre suas estruturas tridimensionais estão disponíveis somente para uma pequena fração destas proteínas. Este fato evidencia a necessidade da criação de métodos automáticos para a predição de estruturas terciárias de proteínas a partir de suas estruturas primárias. Conseqüentemente, ferramentas computacionais são utilizadas para o tratamento, seleção e análise destes dados. Atualmente, um novo método de aprendizado de máquina denominado Máquina de Suporte Vetorial (MSV) tem superado métodos tradicionais como as Redes Neurais Artificiais (RNA) no tratamento de problemas de classicação. Nesta dissertação utilizamos as MSV para a classicação automática de proteínas. A principal contribuição deste trabalho foi a metodologia proposta para o tratamen
The three-dimensional structure of a protein is directly related to its function. Many projects of genetic sequence analysis accumulate a great number of protein sequences whose primary and secondary structures are known. However, the information on its three-dimensional structures are available only for a small fraction of these proteins. This fact evidences the necessity of creation of automatic methods for the prediction of tertiary protein structures from its primary structures. Consequently, computational tools are used for the treatment, election and analysis of these data. Currently, a new method of machine learning called Support Vector Machine (SVM) has surpassed traditional methods as Artificial Neural Networks (ANN) in the treatment of classication problems. In this master thesis we use the SVM for the automatic protein classication. The main contribution of this work was the methodology proposal for the treatment of the problem. This methodology consists in composing the support vectors with the v
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Delezoide, Bertrand. "Modèles d'indéxation multimédia pour la description automatique de films de cinéma". Paris 6, 2006. http://www.theses.fr/2006PA066108.
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Peng, Danilo. "Application of machine learning in 5G to extract prior knowledge of the underlying structure in the interference channel matrices". Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252314.
Testo completoAbstract (sommario):
The data traffic has been growing drastic over the past few years due to digitization and new technologies that are introduced to the market, such as autonomous cars. In order to meet this demand, the MIMO-OFDM system is used in the fifth generation wireless network, 5G. Designing the optimal wireless network is currently the main research within the area of telecommunication. In order to achieve such a system, multiple factors has to be taken into account, such as the suppression of interference from other users. A traditional method called linear minimum mean square error filter is currently used to suppress the interferences. To derive such a filter, a selection of parameters has to be estimated. One of these parameters is the ideal interference plus noise covariance matrix. By gathering prior knowledge of the underlying structure of the interference channel matrices in terms of the number of interferers and their corresponding bandwidths, the estimation of the ideal covariance matrix could be facilitated. As for this thesis, machine learning algorithms were used to extract these prior knowledge. More specifically, a two or three hidden layer feedforward neural network and a support vector machine with a linear kernel was used. The empirical findings implies promising results with accuracies above 95% for each model.Under de senaste åren har dataanvändningen ökat drastiskt på grund av digitaliseringen och allteftersom nya teknologier introduceras på marknaden, exempelvis självkörande bilar. För att bemöta denna efterfrågan används ett s.k. MIMO-OFDM system i den femte generationens trådlösa nätverk, 5G. Att designa det optimala trådlösa nätverket är för närvarande huvudforskningen inom telekommunikation och för att uppnå ett sådant system måste flera faktorer beaktas, bland annat störningar från andra användare. En traditionell metod som används för att dämpa störningarna kallas för linjära minsta medelkvadratfelsfilter. För att hitta ett sådant filter måste flera olika parametrar estimeras, en av dessa är den ideala störning samt bruskovariansmatrisen. Genom att ta reda på den underliggande strukturen i störningsmatriserna i termer av antal störningar samt deras motsvarande bandbredd, är något som underlättar uppskattningen av den ideala kovariansmatrisen. I följande avhandling har olika maskininlärningsalgoritmer applicerats för att extrahera dessa informationer. Mer specifikt, ett neuralt nätverk med två eller tre gömda lager samt stödvektormaskin med en linjär kärna har använts. De slutliga resultaten är lovande med en noggrannhet på minst 95% för respektive modell.
21
Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Cerca il testo completoAbstract (sommario):
Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
22
McChesney, Charlie. "External Support Vector Machine Clustering". ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/409.
Testo completoAbstract (sommario):
The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters.
23
Armond, Kenneth C. Jr. "Distributed Support Vector Machine Learning". ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.
Testo completoAbstract (sommario):
Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives and 500 negatives, for example) can be managed on a PC without hard-drive thrashing. Training sets of 10,000 however, simply cannot be managed with PC-based resources. For this reason most SVM implementations must contend with some kind of chunking process to train parts of the data at a time (10 chunks of 1000, for example, to learn the 10,000). Sequential and multi-threaded chunking methods provide a way to run the SVM on large datasets while retaining accuracy. The multi-threaded distributed SVM described in this thesis is implemented using Java RMI, and has been developed to run on a network of multi-core/multi-processor computers.
24
Zigic, Ljiljana. "Direct L2 Support Vector Machine". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4274.
Testo completoAbstract (sommario):
This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative.
Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's constrained system of equations. This solver shows significant speedup compared to several other state-of-the-art algorithms. The training time improvement is achieved at no cost, in other words, the accuracy is kept at the same level. All the experiments that support this claim were conducted on various datasets within the strict double cross-validation scheme. DL2 SVM solved with NN ISDA has faster training time on both medium and large datasets.
In addition to a comprehensive DL2 SVM model we introduce and derive its three variants. Three different solvers for the DL2's system of linear equations with nonnegativity constraints were implemented, presented and compared in this dissertation.
25
Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines". Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.
Testo completoAbstract (sommario):
Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The objective of this thesis is to compare the performance of Neural Networks (NN) and Support Vector Machines (SVM) in predicting the secondary structure of 62 globular proteins from their primary sequence. For each NN and SVM, we created six binary classifiers to distinguish between the classes’ helices (H) strand (E), and coil (C). For NN we use Resilient Backpropagation training with and without early stopping. We use NN with either no hidden layer or with one hidden layer with 1,2,...,40 hidden neurons. For SVM we use a Gaussian kernel with parameter fixed at = 0.1 and varying cost parameters C in the range [0.1,5]. 10- fold cross-validation is used to obtain overall estimates for the probability of making a correct prediction. Our experiments indicate for NN and SVM that the different binary classifiers have varying accuracies: from 69% correct predictions for coils vs. non-coil up to 80% correct predictions for stand vs. non-strand. It is further demonstrated that NN with no hidden layer or not more than 2 hidden neurons in the hidden layer are sufficient for better predictions. For SVM we show that the estimated accuracies do not depend on the value of the cost parameter. As a major result, we will demonstrate that the accuracy estimates of NN and SVM binary classifiers cannot distinguish. This contradicts a modern belief in bioinformatics that SVM outperforms other predictors.
26
Kuang, Zhanghui, e 旷章辉. "Learning structural SVMs and its applications in computer vision". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206663.
Testo completoAbstract (sommario):
Many computer vision problems involve building automatic systems by extracting complex high-level information from visual data. Such problems can often be modeled using structural models, which relate raw input variables to structural high-level output variables. Structural support vector machine is a discriminative method for learning structural models. It allows a flexible feature construction with good robustness against overfitting, and thus provides state-of-the-art prediction accuracies for structural prediction tasks in computer vision.
This thesis first studies the application of structural SVMs in interactive image segmentation. A novel interactive image segmentation technique that automatically learns segmentation parameters tailored for each and every image is proposed. Unlike existing work, the proposed method does not require any offline parameter tuning or training stage, and is capable of determining image-specific parameters according to some simple user interactions with the target image. The segmentation problem is modeled as an inference of a conditional random field (CRF) over a segmentation mask and the target image.
This CRF is parametrized by the weights for different terms (e.g., color, texture and smoothing). These weight parameters are learned via a one-slack structural SVM, which is solved using a constraint approximation scheme and the cutting plane algorithm. Experimental results show that the proposed method, by learning image-specific parameters automatically, outperforms other state-of-the-art interactive
image segmentation techniques.
This thesis then uses structural SVMs to speed up large scale relatively-paired space analysis. A new multi-modality analysis technique based on relatively-paired observations from multiple modalities is proposed. Relative-pairing information is encoded using relative proximities of observations in a latent common space. By building a discriminative model and maximizing a distance margin, a projection function that maps observations into the latent common space is learned for each modality. However, training based on large scale relatively-paired observations could be extremely time consuming. To this end, the training is reformulated as learning a structural model, which can be optimized by the cutting plane algorithm where only a few training samples are involved in each iteration. Experimental results validate the effectiveness and efficiency of the proposed technique.published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
27
Ryberg, Ann-Britt. "Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures". Doctoral thesis, Linköpings universitet, Mekanik och hållfasthetslära, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140875.
Testo completoAbstract (sommario):
Multidisciplinary design optimization (MDO) can be used in computer aided engineering (CAE) to efficiently improve and balance performance of automotive structures. However, large-scale MDO is not yet generally integrated within automotive product development due to several challenges, of which excessive computing times is the most important one. In this thesis, a metamodel-based MDO process that fits normal company organizations and CAE-based development processes is presented. The introduction of global metamodels offers means to increase computational efficiency and distribute work without implementing complicated multi-level MDO methods. The presented MDO process is proven to be efficient for thickness optimization studies with the objective to minimize mass. It can also be used for spot weld optimization if the models are prepared correctly. A comparison of different methods reveals that topology optimization, which requires less model preparation and computational effort, is an alternative if load cases involving simulations of linear systems are judged to be of major importance. A technical challenge when performing metamodel-based design optimization is lack of accuracy for metamodels representing complex responses including discontinuities, which are common in for example crashworthiness applications. The decision boundary from a support vector machine (SVM) can be used to identify the border between different types of deformation behaviour. In this thesis, this information is used to improve the accuracy of feedforward neural network metamodels. Three different approaches are tested; to split the design space and fit separate metamodels for the different regions, to add estimated guiding samples to the fitting set along the boundary before a global metamodel is fitted, and to use a special SVM-based sequential sampling method. Substantial improvements in accuracy are observed, and it is found that implementing SVM-based sequential sampling and estimated guiding samples can result in successful optimization studies for cases where more conventional methods fail.
28
Wen, Tong 1970. "Support Vector Machine algorithms : analysis and applications". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.
Testo completoAbstract (sommario):
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.Includes bibliographical references (p. 89-97).
Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs solve classification problems by learning from training examples. From the geometry, it is easy to formulate the finding of SVM classifiers as a linearly constrained Quadratic Programming (QP) problem. However, in practice its dual problem is actually computed. An important property of the dual QP problem is that its solution is sparse. The training examples that determine the SVM classifier are known as support vectors (SVs). Motivated by the geometric derivation of the primal QP problem, we investigate how the dual problem is related to the geometry of SVs. This investigation leads to a geometric interpretation of the scaling property of SVMs and an algorithm to further compress the SVs. A random model for the training examples connects the Hessian matrix of the dual QP problem to Wishart matrices. After deriving the distributions of the elements of the inverse Wishart matrix Wn-1(n, nI), we give a conjecture about the summation of the elements of Wn-1(n, nI). It becomes challenging to solve the dual QP problem when the training set is large. We develop a fast algorithm for solving this problem. Numerical experiments show that the MATLAB implementation of this projected Conjugate Gradient algorithm is competitive with benchmark C/C++ codes such as SVMlight and SvmFu. Furthermore, we apply SVMs to time series data.
(cont.) In this application, SVMs are used to predict the movement of the stock market. Our results show that using SVMs has the potential to outperform the solution based on the most widely used geometric Brownian motion model of stock prices.
by Tong Wen.
Ph.D.
29
Liu, Yufeng. "Multicategory psi-learning and support vector machine". Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1085424065.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Ohio State University, 2004.Title from first page of PDF file. Document formatted into pages; contains x, 71 p.; also includes graphics Includes bibliographical references (p. 69-71). Available online via OhioLINK's ETD Center
30
Tsang, Wai-Hung. "Scaling up support vector machines /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.
Testo completo
31
Perez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data". Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.
Testo completoAbstract (sommario):
Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.
32
Chen, Xiujuan. "Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/26.
Testo completoAbstract (sommario):
The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system. T1FFSVM can be improved by tuning the fuzzy membership functions of linguistic variables using genetic algorithms. The improved model is called GFFSVM. To better handle uncertainties existing in fuzzy MFs and in classification data, T1FFSVM can also be improved by applying type-2 fuzzy logic to construct a type-2 fuzzy classifier fusion model (T2FFSVM). T1FFSVM, GFFSVM, and T2FFSVM use accuracy as a classifier performance measure. AUC (the area under an ROC curve) is proved to be a better classifier performance metric. As a comparison study, AUC-based classifier fusion models are also proposed in the dissertation. The experiments on biomedical datasets demonstrate promising performance of the proposed classifier fusion models comparing with the individual composing classifiers. The proposed classifier fusion models also demonstrate better performance than many existing classifier fusion methods. The dissertation also studies one interesting phenomena in biology domain using machine learning and classifier fusion methods. That is, how protein structures and sequences are related each other. The experiments show that protein segments with similar structures also share similar sequences, which add new insights into the existing knowledge on the relation between protein sequences and structures: similar sequences share high structure similarity, but similar structures may not share high sequence similarity.
33
Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units". ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.
Testo completoAbstract (sommario):
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.
34
Sandoval, Rodríguez Rodrigo Antonio. "Metodología de clasificación dinámica utilizando Support Vector Machine". Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/102921.
Testo completoAbstract (sommario):
Esta investigación se centra en el problema de clasificación, por medio de confeccionar una metodología que permita detectar y modelar cambios en los patrones que definen la clasificación en el tiempo, en otras palabras, clasificación dinámica.
La metodología desarrollada propone utilizar los resultados obtenidos en un periodo de tiempo para la construcción del modelo al siguiente periodo. Para ello se utilizaron dos modelos de clasificación distintos; el primero de ellos es Support Vector Machine (SVM) con el objetivo de confeccionar la metodología dinámica, que denominaremos Dynamic Support Vector Machine (D-SVM) y el segundo modelo de clasificación es Linear Penalizad SVM (LP-SVM) con la finalidad de que la metodología construida permita la selección de atributos dinámicamente. Los parámetros utilizados en el modelo de clasificación son; las ventanas de tiempo, ponderadores de relevancia, penalización de los errores y la penalización de los atributos (sólo para el modelo con selección de atributos). De los resultados obtenidos, se utiliza la ventana de tiempo que define el mejor modelo de un periodo y junto a los nuevos datos que se obtengan generan el del próximo.
Esta metodología luego fue aplicada a un caso real en una institución gubernamental chilena (INDAP), en el problema de predicción de comportamiento de pago (credit scoring). Para ello se analizaron 4 instancias de tiempo con 9 atributos para el modelo sin selección de atributos y 18 atributos para el modelo con selección. Luego ambos modelos fueron comparados con uno de clasificación estática, es decir, que las 4 instancias de tiempo son unidas como si fuese una data.
Los resultados obtenidos en esta aplicación son levemente superiores a la metodología estática correspondiente y en el caso de la selección de atributos el modelo utiliza una mayor cantidad.
Las conclusiones de esta investigación son que presenta la ventaja de utilizar una menor cantidad de datos a los disponibles, lo que genera modelos más rápidos y que se van adaptando a los cambios de comportamiento que se producen en el tiempo, al descartar los datos más antiguos en la construcción del nuevo modelo. Con respecto al método con selección de atributos, se destaca que no utiliza un modelo exógeno para seleccionar los atributos sino que el modelo estima los atributos necesarios para cada periodo de tiempo, por lo que se tiene un modelo más estable y generalizado; además se logra obtener información de cómo la relevancia de los atributos cambia en el tiempo. Sobre los resultados se concluye que la metodología D-SVM con y sin selección de atributos es al menos tan buena como los métodos actuales de clasificación.
35
Jia, Ke. "Structured support vector machines learning and application in computer vision". Phd thesis, 2012. http://hdl.handle.net/1885/150821.
Testo completoAbstract (sommario):
Image labeling tasks have been a long standing challenge in computer vision. In recent years, Markov /Conditional Random Fields (MRFs/CRFs) have gained popularity for the concept of "structured" learning, by defining proper pairwise potential functions to represent the spatial correlations among neighboring pixels. In this thesis, we propose an alternative discriminative approach to MRFs/CRFs by extending the max-margin principle to incorporate the spatial correlations. In particular, by explicitly enforcing the submodular condition, graph cuts is conveniently integrated as the inference engine to attain the optimal label assignment efficiently. Our approach allows learning a model with thousands of parameters, which is further facilitated by parallel computation in the learning phase. In addition to node and edge feature functions for enforcing local label consistency, our algorithm is shown to be capable to readily incorporating higher-order scene context. As image labeling focusing on prediction problems of discrete label space, known as the classification, the later part of the thesis moves on to a more gen{u00AD} eral task, the structured Support Vector Regression (SVR). Beside the unary features which are adopted in traditional SVR algorithms, the objective function in our framework considers both label information and pairwise features, helping to achieve better cross-smoothing over neighboring pixels. With the bundle method, we effectively reduce the number of constraints and alleviate the adverse effect of outliers, leading to an efficient and robust learning algorithm. Moreover, we derive the dual form of the structured SVR algorithm to fit in non-linear cases via using kernel method. Another candidate approach of matching kernels is also introduced to simplify the kernel-version algorithm. Despite classic Support Vector Machines (SVMs) are normally used for learning the feature weights in label prediction tasks, the ability of its mining hidden information from data is not limited in this area. The thesis explores on the topic of specialized structured SVMs in the fourth part, including two frameworks targeting on image matching founded stereo and image segmentation based on curve evolution. The basic approaches in these two fields have the common background of unsupervised learning of structured data, which leaves some parameters to manually tune. The specialized SVMs algorithms modify the discriminative function of max-margin learning to fit the certain application scene, and discover the best values of parameters from the ground truth. Furthermore, as the approach can automatically find out proper parameters, some of the feature representation has also been improved in order to bring in self-adaptive parameters for flexibility. Other max-margin learning related strategies like slack rescaling are also discussed in this part. We show some real-world applications using the structured Support Vector Machines (SSVMs), consisting of classification, regression and specialized SSVMs. The approaches perform competitively comparing to the state-of-the{u00AD}art image processing methods. Finally, we discuss some future directions in the field of structured max{u00AD} margin learning, such as efficient MRFs inference engine, joint feature spaces and other unsupervised approaches potentially being learnt.
36
SARI, IRAWATI NURMALA, e 金愛容. "HUMAN POSE TRACKING USING ONLINE LATENT STRUCTURED SUPPORT VECTOR MACHINE". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/69983765964641460788.
Testo completoAbstract (sommario):
碩士國立臺灣科技大學
資訊工程系
103
Human pose tracking in a video is a challenging problem and a desirable requirement in many applications. The problem is challenging in realistic scenes due to complicated movement, occlusion, a lighting change, and etc. We propose an online learning approach for tracking human pose using latent structured SVM. Firstly, we initialize body and latent parts, then we train the model by using a four-stage training process of latent structured SVM. We update the model for each image sequence of video during tracking process. To solve the problem of occlusion, we use body part detection by Prize-Collecting Steiner Tree algorithm (PCST). The experimental results veri ed that our proposed method outperforms the other state-of-the-art human pose approaches.
37
Sie, Man-ru, e 謝嫚如. "A robust visual tracking system based on Structured Support Vector Machine". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/21923974802709508747.
Testo completoAbstract (sommario):
碩士國立臺灣科技大學
資訊工程系
102
Object tracking has been studied broadly as image processing issue for the past few years and the main purpose continually captures the object’s character. It can be applied to video editing, video surveillance, video compression, video retrieval, and etc. But when tracking the objecting, sometimes we lose the object’s information due to frequent occlusions, disappeared object, similar target appearances, missed detection and illumination change. We provide a system to directly predict the next frame’s position with changing and immediately refresh the system by combining learn with track. It defines that the first frame of video has original object and position and builds the original tracking model according to structured output SVM. To track every frame, system uses the last position to calculate and track range which if exists object or not. After tracking the object, and transferring the scale. System uses SIFT+RANSAC to match between rectangular window and object before oversegmentation of rectangular window. After building all of the segmented sub regions to undirected graph, we have to find out the continuous set of the bestscore in order to calculate the area having target object in the rectangular window. Therefore, we turn the issue into Prize-collecting Steiner Tree (PCST) and find out the continuous set of the best-score and aims the rectangular window of object to refresh structured output SVM tracking model and frame position. After estimating, the experimental data compared to the recent methods is better than others.
38
He, Kun. "Stochastic functional descent for learning Support Vector Machines". Thesis, 2014. https://hdl.handle.net/2144/14104.
Testo completoAbstract (sommario):
We present a novel method for learning Support Vector Machines (SVMs) in the online setting. Our method is generally applicable in that it handles the online learning of the binary, multiclass, and structural SVMs in a unified view.
The SVM learning problem consists of optimizing a convex objective function that is composed of two parts: the hinge loss and quadratic regularization. To date, the predominant family of approaches for online SVM learning has been gradient-based methods, such as Stochastic Gradient Descent (SGD). Unfortunately, we note that there are two drawbacks in such approaches: first, gradient-based methods are based on a local linear approximation to the function being optimized, but since the hinge loss is piecewise-linear and nonsmooth, this approximation can be ill-behaved. Second, existing online SVM learning approaches share the same problem formulation with batch SVM learning methods, and they all need to tune a fixed global regularization parameter by cross validation. On the one hand, global regularization is ineffective in handling local irregularities encountered in the online setting; on the other hand, even though the learning problem for a particular global regularization parameter value may be efficiently solved, repeatedly solving for a wide range of values can be costly.
We intend to tackle these two problems with our approach. To address the first problem, we propose to perform implicit online update steps to optimize the hinge loss, as opposed to explicit (or gradient-based) updates that utilize subgradients to perform local linearization. Regarding the second problem, we propose to enforce local regularization that is applied to individual classifier update steps, rather than having a fixed global regularization term.
Our theoretical analysis suggests that our classifier update steps progressively optimize the structured hinge loss, with the rate controlled by a sequence of regularization parameters; setting these parameters is analogous to setting the stepsizes in gradient-based methods. In addition, we give sufficient conditions for the algorithm's convergence. Experimentally, our online algorithm can match optimal classification performances given by other state-of-the-art online SVM learning methods, as well as batch learning methods, after only one or two passes over the training data. More importantly, our algorithm can attain these results without doing cross validation, while all other methods must perform time-consuming cross validation to determine the optimal choice of the global regularization parameter.
39
Meng, Chao-Hong, e 孟昭宏. "Phone Recognition using Structural Support Vector Machine". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/45917295138142120792.
Testo completo
40
Rahmah, Dini Nuzulia, e 林娣美. "Object Tracking via Structured Output Support Vector Machine and Prize-Collecting Steiner Tree". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/cj8qbx.
Testo completoAbstract (sommario):
碩士國立臺灣科技大學
資訊工程系
102
Object tracking in video is a challenging problem in various applications, such as video editing, video surveillance, video compression, video retrieval, and etc. Tracking object is in general not trivial due to loss of information caused by frequent occlusions, similar target appearances, missed detection, inaccurate responses and illumination change. In this thesis, we present a novel object video tracking algorithm via structured output prediction classifier integrated with Prize-Collecting Steiner Tree (PCST). Given an initial bounding box with its position, we first divide it into sub-blocks with a predefined size. And then we extract the features from each sub-blocks with a structured output prediction classifier. We treat the sub-blocks obtained from the initial bounding box as positive samples and then randomly choose negative samples from search windows defined by the specific area around the bounding box. We obtain prediction scores for each sub-blocks both from positive and negative samples. After that, we construct a region-graph with sub-blocks as nodes and classifier's score as weight to detect the target object in each frame. We then employ PCST to obtain the optimal solution for tracking the object in the consecutive video. Our experimental results show that the proposed method outperforms several state-of-the-art object tracking algorithms.
41
Huang, Jin-Nan, e 黃進南. "Prediction of Protein Tertiary Structure-Using Support Vector Machine". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/78285365269604260437.
Testo completoAbstract (sommario):
碩士樹德科技大學
資訊管理研究所
94
Torsion angle is important factor of influence the protein structure. If we can predict torsion angle correct. It is useful for determining the structure of the protein. We can understand the protein function after determined structure of the protein. Traditional X-rays diffraction and nuclear magnetic resonance (NMR) can find out the protein structure correctly, but they must spend a lot of time and cost. Then people use computer to calculate and predict, reduce a lot of time and cost. The purpose of this research is that predict the tertiary structure of main chain of protein. First, use BLAST(Basic Local Alignment Search Tool) to find out the homology sequences(train sets) which target protein(test set) and create PSSM (Position Specific Scoring Matrix). After coded(PSSM and second structure), use SVM(Support Vector Machine) to train and predict torsion angle PHI、PSI、OMEGA and three bond angle. Then take the result of predicted into the rotation formula and calculate the 3D coordinate of atom. Evaluate the experiment, calculate the RMSD(Root Mean Square Deviation) of CA atom. Final take experiment proteins of other paper to predict and compare. The results show, sequence’s identity between test set and train set more high the results more better. And still there are a lot of places that can be improved in the future.
42
Kuo, Wang Chih, e 王誌國. "Hybrid Face Detection System – use of Maximal-margin Spherical-structured One-class Support Vector Machine". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/jejtw2.
Testo completoAbstract (sommario):
碩士國立高雄應用科技大學
資訊管理研究所碩士班
101
Face detection – indicates the face region from a given static/dynamic images – is an important step before face recognition. Those face regions can be any size, position, angle, and lighting condition. The issue of face detection problem is to correctly indicate those face regions under such complex environment. Hybrid based methodology has been succeeded in several area. Sometimes, one method cannot completely solve the problem but can make the problem easily to solve by another method. There are thee famous face detection methodology: color based, morphological image processing based and neural network based face detection methodology, and each was suffered from their inherent shortcoming. Here we propose a hybrid based face detection methodology that composed of above three face detection methodology. We also propose a new maximal-margin single-class support vector machine as the kernel classifier in our methodology
43
Lin, Yen-Hsiu, e 林延修. "A Maximal Margin Sphere-structure Multi-class Support Vector Machine". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/33377008272669819195.
Testo completoAbstract (sommario):
碩士國立成功大學
資源工程學系碩博士班
94
Support vector machine is a maximal margin classifier, which finds the maximal margin between the two classes and uses the hyper-plane right located in the middle of the maximal margin to distinguish the class of the input data. It does not consider the distribution in each class. In order to take the information of data distribution into consideration, our approach uses the support vector data description, introduced by Tax et al, to seek hyper-spheres that tightly enclose the data for each class. The hyper-spheres vary with the distribution (e.g. location, density... etc.) of each class, so those hyper-spheres indeed character some distributive properties of each class. Then we propose some similarity functions to determine the similarity between a data point and each hyper-sphere. The data point will be classified as the class (hyper-sphere) with maximal similarity. In addition, we combine support vector data description with the concept of maximal margin. Experimental results show that the proposed method is better than support vector machine on some benchmark datasets, and the combination of support vector data description with the concept of maximal margin can effectively improve the classification accuracies.
44
Tsai, Chi-Lung, e 蔡佶龍. "Sequence Similarity and Support Vector Machine for Protein Secondary Structure Prediction". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/71631477154804502560.
Testo completoAbstract (sommario):
碩士義守大學
資訊管理學系碩士班
92
The majority of human coding regions have been sequenced and several genome sequencing projects have been completed. With the growth of large-scale sequencing data, an efficient approach to analyze protein is more important. Protein function and structure are foundations for drug design and protein-based product. However, it’s difficult to predict protein function and structure (three-dimension) directly from protein sequence. Therefore, analyzing protein secondary structure is indispensable. In the previous work, researchers always focused on classifying three states of protein secondary structure : helix, strand and coil classes. It’s a common classification problem for the prediction of protein secondary structure. Comparing with other machine learning methods for this problem, many studies usually ignore the protein local sequence/structure properties. It concerns the accuracy of prediction because there exists a large number of proteins that are homologous but whose sequences are only remotely related. In this thesis, we propose to use sequence similarity and Support Vector Machines (SVMs) to predict protein secondary structure. First, we try to encode the amino acids sequences( RS126 and CB513 ) and transform sequence segments into vectors for training. Second, we construct the SVM classifiers for classifying each residue of each sequence into the 3 secondary structure classes (i.e. H, E, or C). SVM has been successfully applied in pattern recognition problem. SVMs are learning systems that use a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. It’s very suitable to compute with large-scale protein sequences.
45
Chang, Chia-chieh, e 張家傑. "A Study of RNA Structure Automatic Classification by using Support Vector Machine". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/07381169339380489502.
Testo completoAbstract (sommario):
碩士修平技術學院
電機工程研究所
97
In this study the RNA structures is predicted by support vector machine (SVM). The source of RNAs comes from SCOR (Structural Classification of RNA), which was used to feed into SVM for training and testing. In our study, the features of RNAs are extracted and coded to demonstrate the feasibility of prediction by using SVM. RNAs play very important roles in the biological macromolecules. Compared with DNA, the kinds of RNA are more and the structures are much complex than DNA also. Though different kinds of RNAs have some common structures but significant differences are also exist. And the differences make wide range of biological functions. In this study, the intelligent learning system is introduced to provide the help of bioinformatics.
46
Chou, Yu-Yu, e 周宥宇. "Spoken Document Summarization : with Structural Support Vector Machine，Domain Adaptation and Abstractive Summarization". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/48591868287184216440.
Testo completo
47
Lyu, Shing Hermes, e 呂行. "A Semi-automatic Computer Expressive Music Performance System Using Structural Support Vector Machine". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/78460609550247011906.
Testo completoAbstract (sommario):
碩士國立臺灣大學
電機工程學研究所
102
Computer generated music is known to be robotic and inexpressive. A computer system that can generate expressive performance potentially has significant impact on music production industry, personalized entertainment or even art. In this paper, we have designed and implemented a system that can generate expressive performance using structural support vector machine with hidden Markov model output (SVM-HMM). We recorded six sets of Muzio Clementi''s Sonatina Op.36 performed by six graduate students. The recordings and scores are manually split into phrases and had their musical features automatically extracted. Using the SVM-HMM algorithm, a mathematical model of expressive performance knowledge is learned from these features. The trained model can generate expressive performances for previously unseen scores (with user-assigned phrasings). The system currently supports monophonic music only. Subjective test shows that the computer generated performances still cannot achieve the same level of expressiveness of human performers, but quantitative similarity measures show that the computer generated performances are much similar to human performances than inexpressive MIDIs.
48
Cao, Yingfang. "Bayesian based structural health management and an uncertainty analysis technique utilizing support vector machine". 2007. http://www.lib.ncsu.edu/theses/available/etd-05032007-225421/unrestricted/etd.pdf.
Testo completo
49
Tsai, Lung-Piao, e 蔡龍表. "Protein Tertiary Structure Analysis－Using Support Vector Regression Machine to Predict Residue-wise Contact Order". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/70646198386140390955.
Testo completoAbstract (sommario):
碩士國立高雄應用科技大學
資訊管理研究所碩士班
96
A major challenge in structural bioinformatics is the prediction of protein structure and function from primary amino acid sequences. This problem becomes more pressing now as the protein sequence-structure gap is widening rapidly as a result of the completion of large-scale genome sequencing projects. Recent prediction of protein tertiary structure in bioinformatics field is more popular, because is the shape of each protein tertiary structure is different. The protein is the major components that impact on organisms and the organism will sicken by abnormal protein. Therefore, many industries want to find more protein tertiary structure to help them to develop new medicine. The traditional methods use X-Ray and NMR (Nuclear Magnetic Resonance) to determine the protein tertiary structure. However, there methods are time-consuming. Therefore, we need some method to speed up the estimation. Using the computer's ability to help the protein structure analysis is a good way. Residue-wise Contact Order (RWCO) is a new kind of one-dimensional protein structure representing the extent of long-range contacts. This study will adopt ν-SVR to predict Residue-wise Contact Orders, and compare the results between the ε-SVR and ν-SVR.
50
Lin, Yian-Lian, e 林延璉. "A New Methodology for Auto Document Category by UsingSentence Structure Model and Support Vector Machine". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/81490043252726711890.
Testo completoAbstract (sommario):
碩士國立高雄第一科技大學
資訊管理研究所
99
With the popularizing of computers and Internet, digital format replaces traditional format of the papers. More and more enterprises transform to E-business, but many earlier documents are still with traditional format. Those documents can be transformed by optical character recognition technical, but it’s hard to search due to lack of classification. Auto Document Category System can deal with this problem. This paper proposed a new methodology for auto document classification. Document classification means to classify a document based on text into a specific category, such as sports or entertainment, etc. Our methodology simplifies the traditional way for building an auto document classification system. We use sentence structure analysis model (SSAM) to segment terms and obtain the part-of-speech as the feature terms. The feature terms are trained by support vector machine (SVM) to build a prediction model. So the unclassified documents can be imported to compare with the prediction model, and then the category of target document can be obtained. Auto document classification system can integrate with web services architecture as a cloud computing service. For examples, users are unnecessary to manually choose a category or fill in tags for adding an article on blogs or forums. However, most of document datasets are imbalance. Traditional feature value of term (TF or TF-IDF) only performs well in text classification for the balanced dataset. We proposed by using term weighting scheme to improves the performance for text classification for imbalance dataset. Classic4 dataset is used to verify that our methodology is effective, and the F1Value for auto categorizing is 92.6%. We also use Reuter-21578 to test the performance of term weighting scheme for imbalanced dataset. The F1Value of our proposed scheme is 77.2% which is higher than other researches.