An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Nello Cristianini, John Shawe-Taylor, Department of Computer Science Royal Holloway John Shawe-Taylor
Cambridge University Press, 23.3.2000 - 189 sivua
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications.
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The Learning Methodology
Linear Learning Machines
KernelInduced Feature Spaces
Support Vector Machines
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algorithm analysis applications approach bias bound called Chapter choice choosing chosen classification complexity computational consider constraints convergence convex corresponding decision defined Definition depends described developed dimension dimensional discussed distribution dual equal error example exists expression feature space Figure finding finite fixed function Gaussian generalisation give given Hence hyperplane hypothesis important inner product input space introduced iterative kernel known learning learning machines linear linear functions loss mapping margin matrix maximal margin measure method minimise motivate norm Note objective function obtained optimisation problem output parameters particular perceptron performance points positive possible primal probability properties quantity referred regression Remark representation respect result satisfies separable sequence shows simple slack solution solve squares Support Vector Machines techniques Theorem theory training set update variables weight vector zero