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Kernel Methods for Relation Extraction

by Dmitry Zelenko, Chinatsu Aone, Anthony Richardella , 2002
"... We present an application of kernel methods to extracting relations from unstructured natural language sources. ..."
Abstract - Cited by 219 (0 self) - Add to MetaCart
We present an application of kernel methods to extracting relations from unstructured natural language sources.

Learning Multiple Tasks with Kernel Methods

by Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil - Journal of Machine Learning Research , 2005
"... Editor: John Shawe-Taylor We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis sh ..."
Abstract - Cited by 251 (10 self) - Add to MetaCart
Editor: John Shawe-Taylor We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis

A primer on kernel methods

by Koji Tsuda, Bernhard Schölkopf - in Kernel Methods in Computational , 2004
"... 1 A primer on kernel methods ..."
Abstract - Cited by 19 (0 self) - Add to MetaCart
1 A primer on kernel methods

Kernel methods

by Bernhard Schölkopf , 2009
"... (slides) ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
Abstract not found

Kernel methods

by Martin Sewell , 2009
"... The term kernel is derived from a word that can be traced back to c. 1000 and originally meant a seed (contained within a fruit) or the softer (usually edible) part contained within the hard shell of a nut or stone-fruit. The former meaning is now obsolete. It was first used in mathematics when it w ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
The term kernel is derived from a word that can be traced back to c. 1000 and originally meant a seed (contained within a fruit) or the softer (usually edible) part contained within the hard shell of a nut or stone-fruit. The former meaning is now obsolete. It was first used in mathematics when

Kernel methods

by Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jouni Järvinen, Jorma Boberg, T. Pahikkala, E. Tsivtsivadze, A. Airola, J. Järvinen, J. Boberg, E. Tsivtsivadze, A. Airola, J. Järvinen, J. Boberg , 2009
"... Abstract In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost functions and we propose three such cost functions. Further, we propose a kernel-based preference learning algorithm, which we call RankRLS, for minimizing these functions. It is shown that Rank ..."
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Abstract In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost functions and we propose three such cost functions. Further, we propose a kernel-based preference learning algorithm, which we call RankRLS, for minimizing these functions. It is shown

Kernel methods for missing variables

by Alex J. Smola, S. V. N. Vishwanathan, Thomas Hofmann - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics , 2005
"... We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ig-nored by kernel methods: How to systemati-cally deal with incomplete data? Our method can also be applied to problems wit ..."
Abstract - Cited by 77 (3 self) - Add to MetaCart
We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ig-nored by kernel methods: How to systemati-cally deal with incomplete data? Our method can also be applied to problems

Kernel methods for relational learning

by Chad Cumby, Dan Roth , 2003
"... Kernel methods have gained a great deal of popularity in the machine learning commu-nity as a method to learn indirectly in high-dimensional feature spaces. Those interested in relational learning have recently begun to cast learning from structured and relational data in terms of kernel operations. ..."
Abstract - Cited by 79 (4 self) - Add to MetaCart
Kernel methods have gained a great deal of popularity in the machine learning commu-nity as a method to learn indirectly in high-dimensional feature spaces. Those interested in relational learning have recently begun to cast learning from structured and relational data in terms of kernel operations

Kernel independent component analysis

by Francis R. Bach - Journal of Machine Learning Research , 2002
"... We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical propert ..."
Abstract - Cited by 464 (24 self) - Add to MetaCart
properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety

Convolution Kernels on Discrete Structures

by David Haussler , 1999
"... We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes the fa ..."
Abstract - Cited by 506 (0 self) - Add to MetaCart
We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes
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