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ICANN
2010
Springer
15 years 6 months ago
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
Asja Fischer, Christian Igel
WEBI
2010
Springer
15 years 2 months ago
DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Su Yan, Sofien Bouaziz, Dongwon Lee
SDM
2009
SIAM
112views Data Mining» more  SDM 2009»
16 years 2 months ago
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluat...
Frederik Janssen, Johannes Fürnkranz
JMLR
2010
125views more  JMLR 2010»
14 years 11 months ago
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
The presence of asymmetry in the misclassification costs or class prevalences is a common occurrence in the pattern classification domain. While much interest has been devoted to ...
Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra
IJCAI
1997
15 years 6 months ago
Is Nonparametric Learning Practical in Very High Dimensional Spaces?
Many of the challenges faced by the £eld of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and ...
Gregory Z. Grudic, Peter D. Lawrence