An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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.
Estimating the Support of a High-Dimensional Distribution
- B. Schölkopf, John C. Platt, J. Shawe-Taylor, Alex Smola, R. C. Williamson
- Computer ScienceNeural Computation
- 1 July 2001
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.
Kernel Methods for Pattern Analysis
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.
Canonical Correlation Analysis: An Overview with Application to Learning Methods
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.
Support Vector Method for Novelty Detection
- B. Schölkopf, R. C. Williamson, Alex Smola, J. Shawe-Taylor, John C. Platt
- Computer ScienceNIPS
- 29 November 1999
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.
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 introduces…
Large Margin DAGs for Multiclass Classification
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.
Challenges in representation learning: A report on three machine learning contests
On Kernel-Target Alignment
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.
Text Classification using String Kernels
- H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, C. Watkins
- Computer ScienceJournal of machine learning research
- 1 March 2002
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.