Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) ISBN  0262194759 Release Date  15 December 2001 Category  Machine Learning Tags  kernel,   "machine learning",   vector,   optimization,   support vector machine,   support vector,   support vector machines,   regular expressions,   machine,   "support vector",   learning,   virtual machine,   regular,   kernels,   0262194759,   support,   "support vector machines",   machine learning,   "learning with kernels",   regular expression,   linux kernel,   "vector machine",   adaptive,   "support vector machine",   kernel methods,   This book @Amazon  View Tools Google Search Web flazx.com

Description In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years. Other books on Machine Learning Data Analysis, Machine Learning and Applications: Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität ... Data Analysis, and Knowledge Organization) Machine Learning in Document Analysis and Recognition (Studies in Computational Intelligence) Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing) Machine Learning for Multimedia Content Analysis (Multimedia Systems and Applications) Advances in Machine Learning Applications in Software Engineering Semi-Supervised Learning (Adaptive Computation and Machine Learning) Innovations in Machine Learning: Theory and Applications (Studies in Fuzziness and Soft Computing) Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) Machine Learning and Robot Perception (Studies in Computational Intelligence) (Studies in Computational Intelligence) Introduction to Machine Learning (Adaptive Computation and Machine Learning) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) Machine Learning, Neural and Statistical Classification (Ellis Horwood Series in Artificial Intelligence)