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ICML
2005
IEEE
14 years 8 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
ICTAI
2005
IEEE
14 years 1 months ago
Determining the Optimal Number of Clusters Using a New Evolutionary Algorithm
Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. An improper pre-selection for the number of clusters might easily ...
Wei Lu, Issa Traoré
ICA
2007
Springer
14 years 1 months ago
Robust Independent Component Analysis Using Quadratic Negentropy
We present a robust algorithm for independent component analysis that uses the sum of marginal quadratic negentropies as a dependence measure. It can handle arbitrary source densit...
Jaehyung Lee, Taesu Kim, Soo-Young Lee
JMLR
2010
136views more  JMLR 2010»
13 years 2 months ago
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
Variational Bayesian (VB) methods are typically only applied to models in the conjugate-exponential family using the variational Bayesian expectation maximisation (VB EM) algorith...
Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti...