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» Fisher Kernels for Relational Data
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EUROPAR
1997
Springer
15 years 1 months ago
A Relational Approach to the Compilation of Sparse Matrix Programs
Abstract. We present a relational algebra based framework for compiling e cient sparse matrix code from dense DO-ANY loops and a speci cation of the representation of the sparse ma...
Vladimir Kotlyar, Keshav Pingali, Paul Stodghill
TNN
2008
182views more  TNN 2008»
14 years 9 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
97
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COLT
2003
Springer
15 years 2 months ago
Learning from Uncertain Data
The application of statistical methods to natural language processing has been remarkably successful over the past two decades. But, to deal with recent problems arising in this ï¬...
Mehryar Mohri
PKDD
2010
Springer
179views Data Mining» more  PKDD 2010»
14 years 7 months ago
Laplacian Spectrum Learning
Abstract. The eigenspectrum of a graph Laplacian encodes smoothness information over the graph. A natural approach to learning involves transforming the spectrum of a graph Laplaci...
Pannagadatta K. Shivaswamy, Tony Jebara
DIS
2008
Springer
14 years 11 months ago
String Kernels Based on Variable-Length-Don't-Care Patterns
Abstract. We propose a new string kernel based on variable-lengthdon't-care patterns (VLDC patterns). A VLDC pattern is an element of ({}) , where is an alphabet and is the ...
Kazuyuki Narisawa, Hideo Bannai, Kohei Hatano, Shu...