An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press

Support Vector Machine (SVM) is a supervised learning algorithm developed by Vladimir Vapnik and his co-workers at AT&T Bell Labs in the mid 90's. This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. [1] An Introduction to Support Vector Machines and other kernel-based learning methods. In this work In addition, it has been shown that SNP markers in these candidate genes could predict whether a person has CFS using an enumerative search method and the support vector machine (SVM) algorithm [9]. Nello Cristianini, John Shawer-Taylor [2] 数据挖掘中的新方法-支持向量机 邓乃扬， 田英杰 [3] 机器学习. Search for optimal SVM kernel and parameters for the regression model of cadata using rpusvm based on similar procedures explained in the text A Practical Guide to Support Vector Classification. When it comes to classification, and machine learning in general, at the head of the pack there's often a Support Vector Machine based method. It is supported on Linux Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. Cambridge: Cambridge University Press, 2000. Bpnn.py - Written by Neil Schemenauer, bpnn.py is used by an IBM article entitled "An introduction to neural networks". According to Vladimir Vapnik in Statistical Learning Theory (1998), the assumption is inappropriate for modern large scale problems, and his invention of the Support Vector Machine (SVM) makes such assumption unnecessary. In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. PyML focuses on SVMs and other kernel methods. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. An Introduction to Support Vector Machines and other kernel-based learning methods. Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques. CRISTIANINI, N.; SHAWE-TAYLOR, J. Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) .