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» Sampling Techniques for Kernel Methods
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PKDD
2004
Springer
116views Data Mining» more  PKDD 2004»
15 years 3 months ago
Random Matrices in Data Analysis
We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certai...
Dimitris Achlioptas
JMLR
2002
137views more  JMLR 2002»
14 years 9 months ago
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Masashi Sugiyama, Klaus-Robert Müller
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
15 years 10 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
MMM
2009
Springer
186views Multimedia» more  MMM 2009»
15 years 4 months ago
A New Multiple Kernel Approach for Visual Concept Learning
In this paper, we present a novel multiple kernel method to learn the optimal classification function for visual concept. Although many carefully designed kernels have been propose...
Jingjing Yang, Yuanning Li, YongHong Tian, Lingyu ...
ICML
2009
IEEE
15 years 10 months ago
Multi-instance learning by treating instances as non-I.I.D. samples
Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely indep...
Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li