Book Chapter10.1007/3-540-33486-6_8
On Kernel-Target Alignment
Nello Cristianini,John Shawe-Taylor,André Elisseeff,Jaz S. Kandola +3 more
- 03 Jan 2001
- Vol. 14, pp 367-373
TL;DR: 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.
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Abstract: We introduce the notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel and a target function. This quantity captures the degree of agreement between a kernel and a given learning task, and has very natural interpretations in machine learning, leading also to simple algorithms for model selection and learning. We analyse its theoretical properties, proving that it is sharply concentrated around its expected value, and we discuss its relation with other standard measures of performance. Finally we describe some of the algorithms that can be obtained within this framework, giving experimental results showing that adapting the kernel to improve alignment on the labelled data significantly increases the alignment on the test set, giving improved classification accuracy. Hence, the approach provides a principled method of performing transduction.
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Citations
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Learning Deep Architectures for AI
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TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
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Gaussian processes for machine learning
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Semi-Supervised Learning
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Domain Adaptation via Transfer Component Analysis
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References
•Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Nello Cristianini,John Shawe-Taylor +1 more
- 01 Jan 2000
TL;DR: 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.
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf,Alexander J. Smola +1 more
- 01 Dec 2001
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide 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.
10.2K
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An Introduction to Support Vector Machines
Nello Cristianini,John Shawe-Taylor +1 more
- 01 Mar 2000
TL;DR: This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.
Advances in kernel methods: support vector learning
Bernhard Schölkopf,Christopher John Burges,Alexander J. Smola +2 more
- 08 Feb 1999
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
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A Probabilistic Theory of Pattern Recognition
Luc Devroye,László Györfi,Gábor Lugosi +2 more
- 01 Jan 1996
TL;DR: The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.
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