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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TLDR
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software. Expand
Estimating the Support of a High-Dimensional Distribution
TLDR
The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm. Expand
Canonical Correlation Analysis: An Overview with Application to Learning Methods
TLDR
A general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text and compares orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model is presented. Expand
Kernel Methods for Pattern Analysis
TLDR
The lectures will introduce the kernel methods approach to pattern analysis through the particular example of support vector machines for classification and argue that, ignoring the technical requirement of positive semi-definiteness, kernel design is not an unnatural task for a practitioner. Expand
Kernel Methods for Pattern Analysis
TLDR
This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. Expand
An introduction to Support Vector Machines
This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introducesExpand
Support Vector Method for Novelty Detection
TLDR
The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data and is regularized by controlling the length of the weight vector in an associated feature space. Expand
Large Margin DAGs for Multiclass Classification
TLDR
An algorithm, DAGSVM, is presented, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG, which is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms. Expand
Text Classification using String Kernels
TLDR
A novel kernel is introduced for comparing two text documents consisting of an inner product in the feature space consisting of all subsequences of length k, which can be efficiently evaluated by a dynamic programming technique. Expand
On Kernel-Target Alignment
TLDR
The notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel and a target function, is introduced, giving experimental results showing that adapting the kernel to improve alignment on the labelled data significantly increases the alignment on a test set, giving improved classification accuracy. Expand
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