Relevant bibliographies by topics / Binary vector

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  2. Dissertations / Theses
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Academic literature on the topic 'Binary vector'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Binary vector"

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Arai, Kenichi, and Hiroyuki Okazaki. "N-Dimensional Binary Vector Spaces." Formalized Mathematics 21, no. 2 (2013): 75–81. http://dx.doi.org/10.2478/forma-2013-0008.

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Summary The binary set {0, 1} together with modulo-2 addition and multiplication is called a binary field, which is denoted by F2. The binary field F2 is defined in [1]. A vector space over F2 is called a binary vector space. The set of all binary vectors of length n forms an n-dimensional vector space Vn over F2. Binary fields and n-dimensional binary vector spaces play an important role in practical computer science, for example, coding theory [15] and cryptology. In cryptology, binary fields and n-dimensional binary vector spaces are very important in proving the security of cryptographic s
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Fei, Sheng-wei. "Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization." Advances in Mechanical Engineering 9, no. 1 (2017): 168781401668529. http://dx.doi.org/10.1177/1687814016685294.

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In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilit
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Ku, Ning-Yun, Shun-Chieh Chang, and Sha-Hwa Hwang. "Binary Search Vector Quantization." AASRI Procedia 8 (2014): 112–17. http://dx.doi.org/10.1016/j.aasri.2014.08.019.

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ZHANG, LI, WEI-DA ZHOU, TIAN-TIAN SU, and LI-CHENG JIAO. "DECISION TREE SUPPORT VECTOR MACHINE." International Journal on Artificial Intelligence Tools 16, no. 01 (2007): 1–15. http://dx.doi.org/10.1142/s0218213007003163.

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A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are nee
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Tissier, Julien, Christophe Gravier, and Amaury Habrard. "Near-Lossless Binarization of Word Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7104–11. http://dx.doi.org/10.1609/aaai.v33i01.33017104.

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Word embeddings are commonly used as a starting point in many NLP models to achieve state-of-the-art performances. However, with a large vocabulary and many dimensions, these floating-point representations are expensive both in terms of memory and calculations which makes them unsuitable for use on low-resource devices. The method proposed in this paper transforms real-valued embeddings into binary embeddings while preserving semantic information, requiring only 128 or 256 bits for each vector. This leads to a small memory footprint and fast vector operations. The model is based on an autoenco
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Bodmann, Bernhard, My Le, Letty Reza, Matthew Tobin, and Mark Tomforde. "Frame theory for binary vector spaces." Involve, a Journal of Mathematics 2, no. 5 (2010): 589–602. http://dx.doi.org/10.2140/involve.2009.2.589.

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Zhou, Changhe, Liren Liu, and Zhijiang Wang. "Binary-encoded vector–matrix multiplication architecture." Optics Letters 17, no. 24 (1992): 1800. http://dx.doi.org/10.1364/ol.17.001800.

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Cha, Sung-Hyuk, Charles Tappert, and Sungsoo Yoon. "Enhancing Binary Feature Vector Similarity Measures." Journal of Pattern Recognition Research 1, no. 1 (2006): 63–77. http://dx.doi.org/10.13176/11.20.

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Brouwer, A. E. "An inequality in binary vector spaces." Discrete Mathematics 59, no. 3 (1986): 315–17. http://dx.doi.org/10.1016/0012-365x(86)90177-9.

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Upadhyaya, Narayana M., Brian Surin, Kerrie Ramm, et al. "Agrobacterium-mediated transformation of Australian rice cultivars Jarrah and Amaroo using modified promoters and selectable markers." Functional Plant Biology 27, no. 3 (2000): 201. http://dx.doi.org/10.1071/pp99078.

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We report the first successfulAgrobacterium-mediated transformation of Australian elite rice cultivars, Jarrah and Amaroo, using binary vectors with our improved promoters and selectable markers. Calli derived from mature embryos were used as target tissues. The binary vectors contained hph(encoding hygromycin resistance) or bar (encoding herbicide resistance) as the selectable marker gene and uidA (gus) or sgfpS65T as the reporter gene driven by different promoters. Use of Agrobacterium strain AGL1 carrying derivatives of an improved binary vector pWBVec8, wherein the CaMV35S driven hph gene
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Dissertations / Theses on the topic "Binary vector"

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Jiang, Fuhua. "SVM-Based Negative Data Mining to Binary Classification." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/8.

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The properties of training data set such as size, distribution and the number of attributes significantly contribute to the generalization error of a learning machine. A not well-distributed data set is prone to lead to a partial overfitting model. Two approaches proposed in this dissertation for the binary classification enhance useful data information by mining negative data. First, an error driven compensating hypothesis approach is based on Support Vector Machines (SVMs) with (1+k)-iteration learning, where the base learning hypothesis is iteratively compensated k times. This approach prod
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Rogers, Spencer David. "Support Vector Machines for Classification and Imputation." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3215.

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Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data i
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Brady, Patrick. "Internal representation and biological plausibility in an artificial neural network." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311273.

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Andreola, Rafaela. "Support Vector Machines na classificação de imagens hiperespectrais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17894.

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É de conhecimento geral que, em alguns casos, as classes são espectralmente muito similares e que não é possível separá-las usando dados convencionais em baixa dimensionalidade. Entretanto, estas classes podem ser separáveis com um alto grau de acurácia em espaço de alta dimensão. Por outro lado, classificação de dados em alta dimensionalidade pode se tornar um problema para classificadores paramétricos, como o Máxima Verossimilhança Gaussiana (MVG). Um grande número de variáveis que caracteriza as imagens hiperespectrais resulta em um grande número de parâmetros a serem estimados e, geralment
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Axén, Maja, and Jennifer Karlberg. "Binary Classification for Predicting Customer Churn." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171892.

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Predicting when a customer is about to turn to a competitor can be difficult, yet extremely valuable from a business perspective. The moment a customer stops being considered a customer is known as churn, a widely researched topic in several industries when dealing with subscription-services. However, in industries with non-subscription services and products, defining churn can be a daunting task and the existing literature does not fully cover this field. Therefore, this thesis can be seen as a contribution to current research, specially when not having a set definition for churn. A definitio
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Oosthuizen, Surette. "Variable selection for kernel methods with application to binary classification." Thesis, Stellenbosch : University of Stellenbosch, 2008. http://hdl.handle.net/10019.1/1301.

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Thesis (PhD (Statistics and Actuarial Science))—University of Stellenbosch, 2008.<br>The problem of variable selection in binary kernel classification is addressed in this thesis. Kernel methods are fairly recent additions to the statistical toolbox, having originated approximately two decades ago in machine learning and artificial intelligence. These methods are growing in popularity and are already frequently applied in regression and classification problems. Variable selection is an important step in many statistical applications. Thereby a better understanding of the problem being in
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Hedlund, Henrik. "Predicting Satisfaction in Customer Support Chat : Opinion Mining as a Binary Classification Problem." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300165.

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The study explores binary classification with Support Vector Machines as means to predict a satisfaction score based on customer surveys in the customer supportdomain. Standard feature selection methods and their impact on results are evaluated and a feature scoring metric Log Odds Ratio is implemented for addressingasymmetrical class distributions. Results show that the feature selection andscoring methods implemented improve performance significantly. Results alsoshow that it is possible to get decent predictive values on test data based onlimited amount of training observations. However mix
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Helsene, Adam Paul. "Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data." Thesis, North Dakota State University, 2020. https://hdl.handle.net/10365/31823.

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A vertical-style data structure and operations on data in that structure are explored and tested in the domain of sliding window average algorithms for geographical information systems (GIS) data. The approach allows working with data of arbitrary precision, which is centrally important for very large GIS data sets. The novel data structure can be constructed from existing multi-channel image data, and data in the structure can be converted back to image data. While in the new structure, operations such as addition, division, and bit-level shifting can be performed in a parallelized manner. I
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Arnroth, Lukas, and Dennis Jonni Fiddler. "Supervised Learning Techniques : A comparison of the Random Forest and the Support Vector Machine." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274768.

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This thesis examines the performance of the support vector machine and the random forest models in the context of binary classification. The two techniques are compared and the outstanding one is used to construct a final parsimonious model. The data set consists of 33 observations and 89 biomarkers as features with no known dependent variable. The dependent variable is generated through k-means clustering, with a predefined final solution of two clusters. The training of the algorithms is performed using five-fold cross-validation repeated twenty times. The outcome of the training process rev
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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.

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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
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Books on the topic "Binary vector"

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Sahandi, Mohammad Reza. Image compression using vector encoding: Illumination correction, noise reduction, and thresholding of digitized CCTV signals produce binary images which are encoded as vector lists---. 1987.

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Ramsay, James R. Transformation of Nicotiana tabacum cv. Xanthi nc and Samsun NN with Agrobacterium tumefaciens binary vectors containing a chimeric tobacco mosaic virus coat protein gene. 1986.

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Book chapters on the topic "Binary vector"

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Ye, Tao, and Xuefeng Zhu. "Binary Coded Output Support Vector Machine." In Intelligent Computing Theories and Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39482-9_6.

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Cimpoeşu, Mihai, Andrei Sucilă, and Henri Luchian. "A Statistical Binary Classifier: Probabilistic Vector Machine." In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40669-0_19.

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Mattera, Davide, and Francesco Palmieri. "Support Vector Machine for Nonparametric Binary Hypothesis Testing." In Perspectives in Neural Computing. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0811-5_11.

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Kryzhanovskiy, Vladimir, and Irina Zhelavskaya. "Double-Layer Vector Perceptron for Binary Patterns Recognition." In Springer Series in Bio-/Neuroinformatics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09903-3_5.

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Yang, Dan, Yu-Chi Chen, and Shaozhen Ye. "Privacy-Preserving Outsource Computing for Binary Vector Similarity." In Security with Intelligent Computing and Big-data Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76451-1_16.

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Chen, Yu, Hong Li, Yuan Ma, Zhiqiang Shi, and Limin Sun. "Robust Network-Based Binary-to-Vector Encoding for Scalable IoT Binary File Retrieval." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94268-1_5.

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Neschen, Martin. "Hierarchical Binary Vector Quantisation Classifiers for Handwritten Character Recognition." In Informatik aktuell. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79980-8_50.

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Ablameyko, Sergey, and Tony Pridmore. "Binary Image Processing and the Raster to Vector Transformation." In Machine Interpretation of Line Drawing Images. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0789-7_5.

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Jonáš, Martin, and Jan Strejček. "Solving Quantified Bit-Vector Formulas Using Binary Decision Diagrams." In Theory and Applications of Satisfiability Testing – SAT 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40970-2_17.

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Maldonado-Bascón, S., S. Al-Khalifa, and F. López-Ferreras. "Feature reduction using Support Vector Machines for binary gas detection." In Artificial Neural Nets Problem Solving Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_101.

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Conference papers on the topic "Binary vector"

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Long-Jhe Yan and Shaw-Hwa Hwang. "The binary vector quantization." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537296.

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Oparin, G. A., V. G. Bogdanova, and A. A. Pashinin. "Application of binary dynamical systems in the problem of classification of Boolean vectors." In 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.15.

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The article proposes a method based on using binary dynamical systems in the classification problem for Boolean vectors (binary feature vectors). This problem has practical application in various fields of science and industry, for example, bioinformatics, remote sensing of natural objects, smart devices of the Internet of things, etc. Binary synchronous autonomous nonlinear dynamic models with an unknown characteristic matrix are considered. Matrix elements are chosen in such a way that the Boolean reference vectors are equilibrium states of the binary dynamic model. The attraction regions of
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Pfletschinger, Stephan, and David Declercq. "Non-binary coding for vector channels." In 2011 IEEE 12th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2011). IEEE, 2011. http://dx.doi.org/10.1109/spawc.2011.5990410.

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Allred, Rustin W., Richard W. Christiansen, and Douglas M. Chabries. "Adaptive vector quantization for binary images." In International Symposium on Optical Science and Technology, edited by Andrew G. Tescher. SPIE, 2000. http://dx.doi.org/10.1117/12.411536.

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Yi Feng, Lei Huang, and Changping Liu. "Palmprint verification using binary contrast context vector." In 2011 First Asian Conference on Pattern Recognition (ACPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/acpr.2011.6166566.

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Lin, Chih-Min, Sheng-Yu Fu, Ding-Yong Hong, Yu-Ping Liu, Jan-Jan Wu, and Wei-Chung Hsu. "Exploiting Vector Processing in Dynamic Binary Translation." In ICPP 2019: 48th International Conference on Parallel Processing. ACM, 2019. http://dx.doi.org/10.1145/3337821.3337844.

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Zhang, Bin, and Sargur N. Srihari. "Binary Vector Dissimilarity Measures for Handwriting Identification." In Electronic Imaging 2003, edited by Tapas Kanungo, Elisa H. Barney Smith, Jianying Hu, and Paul B. Kantor. SPIE, 2003. http://dx.doi.org/10.1117/12.473347.

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Ghosh, Anjan K. "Binary Vector Multiplications With Nonlinear Optical Interfaces." In 31st Annual Technical Symposium, edited by J. P. Letellier. SPIE, 1987. http://dx.doi.org/10.1117/12.942060.

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Chang, L., and M. M. Bayoumi. "An economical binary tree structure for vector quantization." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150428.

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Song Xue, Xiaojun Jing, Songlin Sun, and Hai Huang. "Binary-decision-tree-based multiclass Support Vector Machines." In 2014 14th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2014. http://dx.doi.org/10.1109/iscit.2014.7011875.

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