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ICANN
2010
Springer
15 years 5 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
AAAI
2007
15 years 6 months ago
Biomind ArrayGenius and GeneGenius: Web Services Offering Microarray and SNP Data Analysis via Novel Machine Learning Methods
Analysis of postgenomic biological data (such as microarray and SNP data) is a subtle art and science, and the statistical methods most commonly utilized sometimes prove inadequat...
Ben Goertzel, Cassio Pennachin, Lúcio de So...
ICML
2003
IEEE
16 years 4 months ago
Incorporating Diversity in Active Learning with Support Vector Machines
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
Klaus Brinker
ECIR
2007
Springer
15 years 5 months ago
Automatic Morphological Query Expansion Using Analogy-Based Machine Learning
Information retrieval systems (IRSs) usually suffer from a low ability to recognize a same idea that is expressed in different forms. A way of improving these systems is to take ...
Fabienne Moreau, Vincent Claveau, Pascale Sé...
AIRS
2010
Springer
15 years 1 months ago
Tuning Machine-Learning Algorithms for Battery-Operated Portable Devices
Machine learning algorithms in various forms are now increasingly being used on a variety of portable devices, starting from cell phones to PDAs. They often form a part of standard...
Ziheng Lin, Yan Gu, Samarjit Chakraborty