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» Random Feature Maps for Dot Product Kernels
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ICANN
1997
Springer
15 years 2 months ago
Kernel Principal Component Analysis
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
ML
2006
ACM
121views Machine Learning» more  ML 2006»
14 years 10 months ago
Model-based transductive learning of the kernel matrix
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
KES
2008
Springer
14 years 10 months ago
Classification and Retrieval through Semantic Kernels
Abstract. This work proposes a family of language-independent semantic kernel functions defined for individuals in an ontology. This allows exploiting wellfounded kernel methods fo...
Claudia d'Amato, Nicola Fanizzi, Floriana Esposito
101
Voted
COLT
2005
Springer
15 years 3 months ago
Leaving the Span
We discuss a simple sparse linear problem that is hard to learn with any algorithm that uses a linear combination of the training instances as its weight vector. The hardness holds...
Manfred K. Warmuth, S. V. N. Vishwanathan
82
Voted
CORR
2010
Springer
123views Education» more  CORR 2010»
14 years 8 months ago
Feature Construction for Relational Sequence Learning
Abstract. We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly...
Nicola Di Mauro, Teresa Maria Altomare Basile, Ste...