Chapter 12

[12:1] Machine Learning: A Probabilistic Perspective §14.1 Kernels Introduction K. Murphy – MIT Press 2012

[12:2] An introduction into protein-sequence annotation A. Muller - Dept. of Biological Sciences, Imperial College Center for Bioinformatics 2002 - http://www.sbg.bio.ic.ac.uk/people/mueller/introPSA.pdf

[12:3] Pattern Recognition and Machine Learning §6.4 Gaussian processes C. Bishop –Springer 2006

[12:4] Introduction to Machine Learning §Nonparametric Regression: Smoothing Models. E. Alpaydin - MIT Press 2007

[12:5] The Elements of Statistical Learning: Data Mining, Inference and Prediction §5.8 Regularization and Reproducing Kernel Hilbert Spaces T. Hastie, R. Tibshirani, J. Friedman - Springer 2001

[12:6] The Elements of Statistical Learning: Data Mining, Inference and Prediction §12.3.2 The SVM as a penalization method. T. Hastie, R. Tibshirani, J. Friedman - Springer 2001

[12:7] A Short Introduction to Learning with Kernels B. Scholkopt, - Max Planck Institut für Biologische Kybernetik A. Smola Australian National University 2005 - http://alex.smola.org/papers/2003/SchSmo03c.pdf

[12:8] Machine Learning for Multimedia Content Analysis §10.1.5 SVM Dual Y. Gong, W. Xu- Springer 2007

[12:9] LIBSVM: A Library for Support Vector Machines C-C Chang, C-J Lin - 2003 - http://www.csie.ntu.edu.tw/~cjlin/libsvm/

[12:10] jLibSvm: Efficient Training of Support Vector Machines in Java D. Soergel - https://github.com/davidsoergel/jlibsvm

[12:11] SVMlight Support Vector Machine implementation in C T. Joachims - Dept. of Computer Science, Cornell University 2008 - http://svmlight.joachims.org/

[12:12] Machine Learning: A Probabilistic Perspective §14.5.3 Kernels, Choosing C K. Murphy – MIT Press 2012

[12:13] Data Mining: Anomaly, Outlier, Rare Event Detection G. Nico – 2014 - http://gerardnico.com/wiki/data_mining/anomaly_detection

[12:14] Machine Learning: A Probabilistic Perspective §14.5.1 Kernels: SVMs for regression K. Murphy - MIT Press 2012

[12:15] Pattern Recognition and Machine Learning §7.1.2 Sparse Kernel Machines; Relation to logistic regression C. Bishop - Springer 2006

[12:16] Very Large SVM Training using Core Vector Machines I. Tsang, J. Kwok, P-M, Cheung - Dept. of Computer Science, The Hong Kong University of Science and Technology - http://www.gatsby.ucl.ac.uk/aistats/fullpapers/172.pdf

[12:17] Training Linear SVMs in Linear Time T. Joachims - Dept. of Computer Science Cornell University - http://www.cs.cornell.edu/people/tj/publications/joachims_06a.pdf