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NIPS
2007
13 years 9 months ago
Random Features for Large-Scale Kernel Machines
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Ali Rahimi, Benjamin Recht
ICIAP
2009
ACM
13 years 5 months ago
Tree Covering within a Graph Kernel Framework for Shape Classification
Abstract. Shape classification using graphs and skeletons usually involves edition processes in order to reduce the influence of structural noise. However, edition distances can no...
François-Xavier Dupé, Luc Brun
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
14 years 7 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
ICANN
2009
Springer
14 years 2 months ago
Using Kernel Basis with Relevance Vector Machine for Feature Selection
This paper presents an application of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works...
Frederic Suard, David Mercier
COLT
2010
Springer
13 years 5 months ago
Inferring Descriptive Generalisations of Formal Languages
In the present paper, we introduce a variant of Gold-style learners that is not required to infer precise descriptions of the languages in a class, but that must find descriptive ...
Dominik D. Freydenberger, Daniel Reidenbach