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» Learning Multiple Latent Variables with Self-Organizing Maps
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PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
13 years 6 months ago
Entropy and Margin Maximization for Structured Output Learning
Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs)....
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm...
FSS
2010
147views more  FSS 2010»
13 years 6 months ago
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentu...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
ICML
2006
IEEE
14 years 8 months ago
Nonstationary kernel combination
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
SAC
2010
ACM
14 years 17 days ago
Data stream anomaly detection through principal subspace tracking
We consider the problem of anomaly detection in multiple co-evolving data streams. In this paper, we introduce FRAHST (Fast Rank-Adaptive row-Householder Subspace Tracking). It au...
Pedro Henriques dos Santos Teixeira, Ruy Luiz Mili...
IROS
2006
IEEE
107views Robotics» more  IROS 2006»
14 years 1 months ago
Learning Sensory-Motor Maps for Redundant Robots
— Humanoid robots are routinely engaged in tasks requiring the coordination between multiple degrees of freedom and sensory inputs, often achieved through the use of sensorymotor...
Manuel Lopes, José Santos-Victor