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Kernel Factory: An ensemble of kernel machines [PDF]

Expert Systems with Applications, 2013
We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by an automatically selected kernel function into a kernel matrix K.
M. BALLINGS, D. VAN DEN POEL
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Advances in kernel methods: support vector learning

, 1999
Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and ...
B. Scholkopf, C. Burges, Alex Smola
semanticscholar   +1 more source

An introduction to kernel-based learning algorithms

IEEE Trans. Neural Networks, 2001
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.
K. Müller, S. Mika, Gunnar Rätsch, K. Tsuda, B. Scholkopf   +4 more
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Reproducing kernel particle methods

, 1995
A new continuous reproducing kernel interpolation function which explores the attractive features of the flexible time-frequency and space-wave number localization of a window function is developed.
Wing Kam Liu, S. Jun, Y. Zhang
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A reliable data-based bandwidth selection method for kernel density estimation

, 1991
We present a new method for data-based selection of the bandwidth in kernel density estimation which has excellent properties. It improves on a recent procedure of Park and Marron (which itself is a good method) in various ways. First, the new method has
S. Sheather, M. C. Jones
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The bound of the kernel

Mathematical Social Sciences, 1992
We provide a better lower bound \(\varepsilon_{**}\) such that the kernel is a subset of the strong \(\varepsilon\)-core if \(\varepsilon\geq\varepsilon_{**}\).
Ching Yu Kan, Chih Chang
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An Introduction to the Theory of Reproducing Kernel Hilbert Spaces

, 2016
Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role in complex analysis, probability, group representation theory, and the theory of integral ...
V. Paulsen, M. Raghupathi
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Partitionable Kernels for Mapping Kernels

2011 IEEE 11th International Conference on Data Mining, 2011
Many of tree kernels in the literature are designed tanking advantage of the mapping kernel framework. The most important advantage of using this framework is that we have a strong theorem to examine positive definiteness of the resulting tree kernels.
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The Analyticity of Kernels

Canadian Journal of Mathematics, 1961
Let V be a paracompact real analytic manifold of dimension n ≥ 1. Following the terminology of the theory of distributions of Schwartz (4), is the linear space of infinitely differentiable functions with compact support in V with the appropriate inductive limit topology, is the Frechet space of infinitely differentiable functions on V, is the dual ...
J. De Barros-Neto, F. E. Browder
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Reinterpreting the Kernel

Journal of Economic Theory, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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