Books on the topic 'Support Vector Machine'

Andreas, Christmann, ed. Support vector machines. New York: Springer, 2008.

Campbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

Joachim, Diederich, ed. Rule extraction from support vector machines. Berlin: Springer, 2008.

Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.

Hamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.

missing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.

Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.

Support vector machines for pattern classification. 2nd ed. London: Springer, 2010.

Joachims, Thorsten. Learning to classify text using support vector machines. Boston: Kluwer Academic Publishers, 2002.

K, Suykens Johan A., Signoretto Marco, and Argyriou Andreas, eds. Regularization, optimization, kernels, and support vector machines. Boca Raton: Taylor & Francis, 2014.

Bernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.

J, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, Mass: MIT Press, 2002.

-W, Lee S., and Verri Alessandro, eds. Pattern recognition with support vector machines: First international workshop, SVM 2002, Niagara Falls, Canada, August 202 : proceedings. Berlin: Springer, 2002.

Martinez-Ramon, Manuel. Support vector machines for antenna array processing and electromagnetics. [San Rafael, Calif.]: Morgan & Claypool Publishers, 2006.

Terzic, Jenny. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach. Heidelberg: Springer International Publishing, 2013.

Léon-Charles, Tranchevent, Moor Bart, Moreau Yves, and SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

Lorenzo, Bruzzone, ed. Kernel methods for remote sensing 1: Data analysis 2. Hoboken, NJ: Wiley, 2009.

Shi, Feng. Learn About Support Vector Machine in R With Data From the Adult Census Income Dataset (1996). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526495471.

Shi, Feng. Learn About Support Vector Machine in Python With Data From the Adult Census Income Dataset (1996). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499585.

Support vector machines applications. Cham [Switzerland]: Springer, 2014.

Jayadeva, Reshma Khemchandani, and Suresh Chandra. Twin Support Vector Machines. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46186-1.

Ma, Yunqian, and Guodong Guo, eds. Support Vector Machines Applications. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02300-7.

Campbell, Colin, and Yiming Ying. Learning with Support Vector Machines. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01552-6.

Murty, M. N., and Rashmi Raghava. Support Vector Machines and Perceptrons. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41063-0.

Lee, Seong-Whan, and Alessandro Verri, eds. Pattern Recognition with Support Vector Machines. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45665-1.

Abe, Shigeo. Support Vector Machines for Pattern Classification. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-098-4.

Wang, Lipo, ed. Support Vector Machines: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b95439.

Diederich, Joachim, ed. Rule Extraction from Support Vector Machines. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2.

Drezet, P. Support vector machines for system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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. Hackensack, New Jersey: World Scientific, 2015.

National Institute of Standards and Technology (U.S.), ed. Support vector machines applied to face recognition. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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. New Jersey: World Scientific, 2011.

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. Hackensack, NJ: World Scientific, 2005.

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. New Jersey: World Scientific, 2011.

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. New Jersey: World Scientific, 2008.

Joachims, Thorsten. Learning to Classify Text Using Support Vector Machines. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0907-3.

Stoean, Catalin, and Ruxandra Stoean. Support Vector Machines and Evolutionary Algorithms for Classification. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06941-8.

Joachims, Thorsten. Learning to Classify Text Using Support Vector Machines. Boston, MA: Springer US, 2002.

International, Conference on Artificial Neural Networks and Genetic Algorithms (2007 Warsaw Poland). Adaptive and natural computing algorithms: 8th international conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007 : proceedings. Berlin: Springer, 2007.

Martínez-Ramón, Manel, and Christos Christodoulou. Support Vector Machines for Antenna Array Processing and Electromagnetics. Cham: Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01692-9.

Drezet, P. Directly optimized support vector machines for classification and regression. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

Support Vector Machines. Springer New York, 2014.

Ma, Yunqian, and Guodong Guo. Support Vector Machines Applications. Springer, 2016.

Support Vector Machine In Chemistry. World Scientific Publishing Company, 2004.

Diederich, Joachim. Rule Extraction from Support Vector Machines. Springer, 2010.

Least squares support vector machines. River Edge, NJ: World Scientific, 2002.

Vandewalle, Joos, Bart De Moor, Tony Van Gestel, Jos De Brabanter, and Johan A. K. Suykens. Least Squares Support Vector Machines. World Scientific Publishing Company, 2003.

Wang, Lipo. Support Vector Machines: Theory and Applications. Springer Berlin / Heidelberg, 2010.

Suykens, Johan A. K., Marco Signoretto, and Andreas Argyriou. Regularization, Optimization, Kernels, and Support Vector Machines. Taylor & Francis Group, 2014.

Suykens, Johan A. K., Marco Signoretto, and Andreas Argyriou. Regularization, Optimization, Kernels, and Support Vector Machines. Taylor & Francis Group, 2014.