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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
14 years 4 months ago
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
Tobias Jung, Peter Stone
TR
2010
204views Hardware» more  TR 2010»
13 years 4 months ago
Anomaly Detection Through a Bayesian Support Vector Machine
This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to ...
Vasilis A. Sotiris, Peter W. Tse, Michael Pecht
PCM
2004
Springer
166views Multimedia» more  PCM 2004»
14 years 3 months ago
Gabor-Kernel Fisher Analysis for Face Recognition
Kernel based methods have been of wide concern in the field of machine learning. This paper proposes a novel Gabor-Kernel Fisher analysis method (G-EKFM) for face recognition, whi...
Baochang Zhang
SSPR
2010
Springer
13 years 8 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
ICIP
1995
IEEE
14 years 11 months ago
3D super-resolution using generalized sampling expansion
A 3D super-resolution algorithm is proposed below, based on a probabilistic interpretation of the ndimensional version of Papoulis' generalized sampling theorem. The algorith...
Hassan Shekarforoush, Marc Berthod, Josiane Zerubi...