Relevant bibliographies by topics / Hidden Markov support vector machine

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Hidden Markov support vector machine'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Hidden Markov support vector machine"

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Sloin, Alba, and David Burshtein. "Support Vector Machine Training for Improved Hidden Markov Modeling." IEEE Transactions on Signal Processing 56, no. 1 (2008): 172–88. http://dx.doi.org/10.1109/tsp.2007.906741.

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ZAKI, NAZAR M., SAFAAI DERIS, and ROSLI M. ILLIAS. "FEATURES EXTRACTION FOR PROTEIN HOMOLOGY DETECTION USING HIDDEN MARKOV MODELS COMBINING SCORES." International Journal of Computational Intelligence and Applications 04, no. 01 (2004): 1–12. http://dx.doi.org/10.1142/s1469026804001161.

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Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher scores that are assigned to the sequence and then use support vector machine in conjunction with the fisher scores for protein homologies detection. In this paper, we revisit the idea of using a discriminative approach, and in particular support vector machines for protein homologies detection. However, in place of the Fisher scoring method, we present a new Hidden Markov Model Combining Scores approach. Six scoring algorithms are combined as a way of extracting features from a protein sequence. Experiments show that our method, improves on previous methods for homologies detection of protein domains.
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LIU, Guanjun. "Fault diagnosis approach based on hidden Markov model and support vector machine." Chinese Journal of Mechanical Engineering (English Edition) 20, no. 05 (2007): 92. http://dx.doi.org/10.3901/cjme.2007.05.092.

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Kumar, Manish, Ruchi Verma, and Gajendra P. S. Raghava. "Prediction of Mitochondrial Proteins Using Support Vector Machine and Hidden Markov Model." Journal of Biological Chemistry 281, no. 9 (2005): 5357–63. http://dx.doi.org/10.1074/jbc.m511061200.

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S, Omprakash. "Coronary artery disease prediction using hidden Markov model based support vector machine." Indian Journal of Science and Technology 13, no. 17 (2020): 1703–13. http://dx.doi.org/10.17485/ijst/v13i17.20.

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Sloukia, Fatima Ezzahra, Rajae Bouarfa, Hicham Medromi, and Mohammed Wahbi. "Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine." International Journal of Network Security & Its Applications 5, no. 3 (2013): 85–97. http://dx.doi.org/10.5121/ijnsa.2013.5308.

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Xia, Tian, and Xuemin Chen. "A Discrete Hidden Markov Model for SMS Spam Detection." Applied Sciences 10, no. 14 (2020): 5011. http://dx.doi.org/10.3390/app10145011.

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.
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Wissel, Tobias, Tim Pfeiffer, Robert Frysch, et al. "Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography." Journal of Neural Engineering 10, no. 5 (2013): 056020. http://dx.doi.org/10.1088/1741-2560/10/5/056020.

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Liu, Bin, Bingquan Liu, Fule Liu, and Xiaolong Wang. "Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/464093.

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Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.
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Firdaniza, Firdaniza, and Jondri Jondri. "Prediksi Trend Pergerakan Harga Saham dengan Hidden Markov Model (HMM) dan Support Vector Machine (SVM)." Jurnal Matematika Integratif 10, no. 1 (2020): 19. http://dx.doi.org/10.24198/jmi.v10.n1.10181.19-24.

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Dissertations / Theses on the topic "Hidden Markov support vector machine"

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Fu, Teng. "IntelliChair : a non-intrusive sitting posture and sitting activity recognition system." Thesis, Abertay University, 2015. https://rke.abertay.ac.uk/en/studentTheses/5b60a500-c3fc-4a79-9028-d7909e01b78c.

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Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.
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Caceres, Carlos Antonio. "Machine Learning Techniques for Gesture Recognition." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52556.

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Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications. In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field. The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer.<br>Master of Science
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Landry, Matthew. "Analysis of Nanopore Detector Measurements using Machine Learning Methods, with Application to Single-Molecule Kinetics." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/533.

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At its core, a nanopore detector has a nanometer-scale biological membrane across which a voltage is applied. The voltage draws a DNA molecule into an á-hemolysin channel in the membrane. Consequently, a distinctive channel current blockade signal is created as the molecule flexes and interacts with the channel. This flexing of the molecule is characterized by different blockade levels in the channel current signal. Previous experiments have shown that a nanopore detector is sufficiently sensitive such that nearly identical DNA molecules were classified successfully using machine learning techniques such as Hidden Markov Models and Support Vector Machines in a channel current based signal analysis platform [4-9]. In this paper, methods for improving feature extraction are presented to improve both classification and to provide biologists and chemists with a better understanding of the physical properties of a given molecule.
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Li, Jinyu. "Soft margin estimation for automatic speech recognition." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26613.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yuan. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Peng, Yingli. "Improvement of Data Mining Methods on Falling Detection and Daily Activities Recognition." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25521.

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With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new  idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.
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Yau, Wai Chee, and waichee@ieee org. "Video Analysis of Mouth Movement Using Motion Templates for Computer-based Lip-Reading." RMIT University. Electrical and Computer Engineering, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20081209.162504.

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This thesis presents a novel lip-reading approach to classifying utterances from video data, without evaluating voice signals. This work addresses two important issues which are • the efficient representation of mouth movement for visual speech recognition • the temporal segmentation of utterances from video. The first part of the thesis describes a robust movement-based technique used to identify mouth movement patterns while uttering phonemes. This method temporally integrates the video data of each phoneme into a 2-D grayscale image named as a motion template (MT). This is a view-based approach that implicitly encodes the temporal component of an image sequence into a scalar-valued MT. The data size was reduced by extracting image descriptors such as Zernike moments (ZM) and discrete cosine transform (DCT) coefficients from MT. Support vector machine (SVM) and hidden Markov model (HMM) were used to classify the feature descriptors. A video speech corpus of 2800 utterances was collected for evaluating the efficacy of MT for lip-reading. The experimental results demonstrate the promising performance of MT in mouth movement representation. The advantages and limitations of MT for visual speech recognition were identified and validated through experiments. A comparison between ZM and DCT features indicates that th e accuracy of classification for both methods is very comparable when there is no relative motion between the camera and the mouth. Nevertheless, ZM is resilient to rotation of the camera and continues to give good results despite rotation but DCT is sensitive to rotation. DCT features are demonstrated to have better tolerance to image noise than ZM. The results also demonstrate a slight improvement of 5% using SVM as compared to HMM. The second part of this thesis describes a video-based, temporal segmentation framework to detect key frames corresponding to the start and stop of utterances from an image sequence, without using the acoustic signals. This segmentation technique integrates mouth movement and appearance information. The efficacy of this technique was tested through experimental evaluation and satisfactory performance was achieved. This segmentation method has been demonstrated to perform efficiently for utterances separated with short pauses. Potential applications for lip-reading technologies include human computer interface (HCI) for mobility-impaired users, defense applications that require voice-less communication, lip-reading mobile phones, in-vehicle systems, and improvement of speech-based computer control in noisy environments.
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Truong, Arthur. "Analyse du contenu expressif des gestes corporels." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0015/document.

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Aujourd’hui, les recherches portant sur le geste manquent de modèles génériques. Les spécialistes du geste doivent osciller entre une formalisation excessivement conceptuelle et une description purement visuelle du mouvement. Nous reprenons les concepts développés par le chorégraphe Rudolf Laban pour l’analyse de la danse classique contemporaine, et proposons leur extension afin d’élaborer un modèle générique du geste basé sur ses éléments expressifs. Nous présentons également deux corpus de gestes 3D que nous avons constitués. Le premier, ORCHESTRE-3D, se compose de gestes pré-segmentés de chefs d’orchestre enregistrés en répétition. Son annotation à l’aide d’émotions musicales est destinée à l’étude du contenu émotionnel de la direction musicale. Le deuxième corpus, HTI 2014-2015, propose des séquences d’actions variées de la vie quotidienne. Dans une première approche de reconnaissance dite « globale », nous définissons un descripteur qui se rapporte à l’entièreté du geste. Ce type de caractérisation nous permet de discriminer diverses actions, ainsi que de reconnaître les différentes émotions musicales que portent les gestes des chefs d’orchestre de notre base ORCHESTRE-3D. Dans une seconde approche dite « dynamique », nous définissons un descripteur de trame gestuelle (e.g. défini pour tout instant du geste). Les descripteurs de trame sont utilisés des poses-clés du mouvement, de sorte à en obtenir à tout instant une représentation simplifiée et utilisable pour reconnaître des actions à la volée. Nous testons notre approche sur plusieurs bases de geste, dont notre propre corpus HTI 2014-2015<br>Nowadays, researches dealing with gesture analysis suffer from a lack of unified mathematical models. On the one hand, gesture formalizations by human sciences remain purely theoretical and are not inclined to any quantification. On the other hand, the commonly used motion descriptors are generally purely intuitive, and limited to the visual aspects of the gesture. In the present work, we retain Laban Movement Analysis (LMA – originally designed for the study of dance movements) as a framework for building our own gesture descriptors, based on expressivity. Two datasets are introduced: the first one is called ORCHESTRE-3D, and is composed of pre-segmented orchestra conductors’ gestures, which have been annotated with the help of lexicon of musical emotions. The second one, HTI 2014-2015, comprises sequences of multiple daily actions. In a first experiment, we define a global feature vector based upon the expressive indices of our model and dedicated to the characterization of the whole gesture. This descriptor is used for action recognition purpose and to discriminate the different emotions of our orchestra conductors’ dataset. In a second approach, the different elements of our expressive model are used as a frame descriptor (e.g., describing the gesture at a given time). The feature space provided by such local characteristics is used to extract key poses of the motion. With the help of such poses, we obtain a per-frame sub-representation of body motions which is available for real-time action recognition purpose
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Van, Rooy Theodore. "Stochastic time series prediction comparison with Markov and support vector machine models." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1439425.

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Okuyucu, Cigdem. "Semantic Classification And Retrieval System For Environmental Sounds." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615114/index.pdf.

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The growth of multimedia content in recent years motivated the research on audio classification and content retrieval area. In this thesis, a general environmental audio classification and retrieval approach is proposed in which higher level semantic classes (outdoor, nature, meeting and violence) are obtained from lower level acoustic classes (emergency alarm, car horn, gun-shot, explosion, automobile, motorcycle, helicopter, wind, water, rain, applause, crowd and laughter). In order to classify an audio sample into acoustic classes, MPEG-7 audio features, Mel Frequency Cepstral Coefficients (MFCC) feature and Zero Crossing Rate (ZCR) feature are used with Hidden Markov Model (HMM) and Support Vector Machine (SVM) classifiers. Additionally, a new classification method is proposed using Genetic Algorithm (GA) for classification of semantic classes. Query by Example (QBE) and keyword-based query capabilities are implemented for content retrieval.
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Fredborg, Johan. "Spam filter for SMS-traffic." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94161.

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Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries. This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by analyzing the accuracy of the filters on stored message data. The report also discusses and compares requirements for hardware versus performance measured by how many messages that can be evaluated in a fixed amount of time. The results from the evaluation shows that a decision tree filter is the best choice of the filters evaluated. It has the highest accuracy as well as a high enough process rate of messages to be applicable. The decision tree filter which was found to be the most suitable for the task in this environment has been implemented. The accuracy in this new implementation is shown to be as high as the implementation used for the evaluation of this filter. Though the decision tree filter is shown to be the best choice of the filters evaluated it turned out the accuracy is not high enough to meet the specified requirements. It however shows promising results for further testing in this area by using improved methods on the best performing algorithms.
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Books on the topic "Hidden Markov support vector machine"

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The nonlinear workbook: Chaos, fractals, cellular automata, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, fuzzy logic with C++, Java and symbolic C++ programs. 6th ed. World Scientific, 2015.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. World Scientific, 2011.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 3rd ed. World Scientific, 2005.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. World Scientific, 2011.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 4th ed. World Scientific, 2008.

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Book chapters on the topic "Hidden Markov support vector machine"

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Tang, Hong, and Ali A. Ghorbani. "Accent Classification Using Support Vector Machine and Hidden Markov Model." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44886-1_65.

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Wang, Li, and Li Li. "Automatic Text Classification Based on Hidden Markov Model and Support Vector Machine." In Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37502-6_27.

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Okada, Takashi, Atsuhiro Takasu, and Jun Adachi. "Bibliographic Component Extraction Using Support Vector Machines and Hidden Markov Models." In Research and Advanced Technology for Digital Libraries. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30230-8_46.

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Gassend, Blaise, Charles W. O’Donnell, William Thies, Andrew Lee, Marten van Dijk, and Srinivas Devadas. "Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11818564_11.

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Wan, Yuehua, Shiming Ji, Yi Xie, Xian Zhang, and Peijun Xie. "Video Program Clustering Indexing Based on Face Recognition Hybrid Model of Hidden Markov Model and Support Vector Machine." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30503-3_57.

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Yang, Lei, Miao He, Junshan Zhang, and Vijay Vittal. "Support Vector Machine Enhanced Markov Model for Short TermWind Power Forecast." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12319-6_3.

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de Souza, César Roberto, and Ednaldo Brigante Pizzolato. "Sign Language Recognition with Support Vector Machines and Hidden Conditional Random Fields: Going from Fingerspelling to Natural Articulated Words." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39712-7_7.

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Morsi, Yos S., Pujiang Shi, and Amal Ahmed Owida. "Breast Cancer." In Biomedical Engineering and Information Systems. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-004-3.ch009.

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Breast cancer is the second most common cancer in the world and is difficult to accurately identify and treat. Diagnostic computational tools can be used effectively, with high degree of accuracy, to recognize and differentiate between the two known types of breast lesion, namely benign and malignant. These modelling tools include artificial intelligence techniques such as Artificial Neural Networks (ANNs), Fuzzy Logic (FL), Hidden Markov Model (HMM) and Support Vector Machines (SVMs). These tools can identify the important features that play pivotal roles in the classification task, and can aid physicians to diagnose and prognosticate breast cancer. Moreover, recent advancement in nanotechnology indicates that with the aid of nanoparticles, nanowires, nanorobots and nanotubes, the disese of breast cancer can be potentially eradicated totally. The chapter highlights the limitations of the current therapies used in breast cancer and discusses the concept of nanotechnology as a possible future therapy.
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Fu, Xiuju, Lipo Wang, GihGuang Hung, and Liping Goh. "Linguistic Rule Extraction from Support Vector Machine Classifiers." In Research and Trends in Data Mining Technologies and Applications. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-271-8.ch010.

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Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from the support vector along each axis, and an SVM decision hyper-curve are searched first. A hyper-rectangular rule is derived from these cross points. The hyper-rectangle is tuned by a tuning phase in order to exclude those out-class data points. Finally, redundant rules are merged to produce a compact rule set. Simultaneously, important attributes could be highlighted in the extracted rules. Rule extraction results from our proposed method could follow SVM classifier decisions very well. We compare the rule extraction results from SVM with RBF kernel function and linear kernel function. Experiment results show that rules extracted from SVM with RBF nonlinear kernel function are with better accuracy than rules extracted from SVM with linear kernel function. Comparisons between our method and other rule extraction methods are also carried out on several benchmark data sets. Higher rule accuracy is obtained in our method with fewer number of premises in each rule.
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Fu, Xiuju, Lipo Wang, GihGuang Hung, and Liping Goh. "Linguistic Rule Extraction from Support Vector Machine Classifiers." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch072.

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Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from the support vector along each axis, and an SVM decision hyper-curve are searched first. A hyper-rectangular rule is derived from these cross points. The hyper-rectangle is tuned by a tuning phase in order to exclude those out-class data points. Finally, redundant rules are merged to produce a compact rule set. Simultaneously, important attributes could be highlighted in the extracted rules. Rule extraction results from our proposed method could follow SVM classifier decisions very well. We compare the rule extraction results from SVM with RBF kernel function and linear kernel function. Experiment results show that rules extracted from SVM with RBF nonlinear kernel function are with better accuracy than rules extracted from SVM with linear kernel function. Comparisons between our method and other rule extraction methods are also carried out on several benchmark data sets. Higher rule accuracy is obtained in our method with fewer number of premises in each rule.
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Conference papers on the topic "Hidden Markov support vector machine"

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Fan, Shi-Xi, Li-Dan Chen, Xuan Wang, and Bu-Zhou Tang. "Shallow parsing with Hidden Markov Support Vector Machines." In 2014 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2014. http://dx.doi.org/10.1109/icmlc.2014.7009716.

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Sloin, Alba, and David Burshtein. "Support Vector Machine Re-scoring of Hidden Markov Models." In 2006 IEEE 24th Convention of Electrical & Electronics Engineers in Israel. IEEE, 2006. http://dx.doi.org/10.1109/eeei.2006.321107.

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Bashashati, Hossein, Rabab K. Ward, and Ali Bashashati. "Hidden Markov Support Vector Machines for Self-Paced Brain Computer Interfaces." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.178.

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Wang, Zhen, and Massimo Piccardi. "A pair hidden Markov support vector machine for alignment of human actions." In 2016 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2016. http://dx.doi.org/10.1109/icme.2016.7552933.

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Wang, Zhen, and Massimo Piccardi. "Dissimilarity-based action recognition with the pair hidden Markov support vector machine." In 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2017. http://dx.doi.org/10.1109/mmsp.2017.8122257.

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Ahmad, Abdul Rahim, Christian Viard-Gaudin, and Marzuki Khalid. "Lexicon-Based Word Recognition Using Support Vector Machine and Hidden Markov Model." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.248.

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Sloukia, F., M. El Aroussi, H. Medromi, and M. Wahbi. "Bearings prognostic using Mixture of Gaussians Hidden Markov Model and Support Vector Machine." In 2013 ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2013. http://dx.doi.org/10.1109/aiccsa.2013.6616438.

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Ali, Samr, and Nizar Bouguila. "Hybrid Generative-Discriminative Generalized Dirichlet-Based Hidden Markov Models with Support Vector Machines." In 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019. http://dx.doi.org/10.1109/ism46123.2019.00050.

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Singh, Gautam B., and Haiping Song. "Comparison of Hidden Markov Models and Support Vector Machines for vehicle crash detection." In 2010 International Conference on Methods and Models in Computer Science (ICM2CS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icm2cs.2010.5706709.

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Das, Deepjoy, and Alok Chakrabarty. "Human gait based gender identification system using Hidden Markov Model and Support Vector Machines." In 2015 International Conference on Computing, Communication & Automation (ICCCA). IEEE, 2015. http://dx.doi.org/10.1109/ccaa.2015.7148386.

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