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143
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ICML
2010
IEEE
15 years 5 months ago
2010
IEEE
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
112
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ICML
2010
IEEE
15 years 5 months ago
2010
IEEE
The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of co...
153
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ICML
2010
IEEE
15 years 5 months ago
2010
IEEE
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...
140
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ICML
2010
IEEE
15 years 5 months ago
2010
IEEE
The importance of bringing causality into play when designing feature selection methods is more and more acknowledged in the machine learning community. This paper proposes a filt...
135
click to vote
ICML
2010
IEEE
15 years 5 months ago
2010
IEEE
When equipped with kernel functions, online learning algorithms are susceptible to the "curse of kernelization" that causes unbounded growth in the model size. To addres...