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JMLR
2010
123views more  JMLR 2010»
13 years 3 months ago
Inductive Principles for Restricted Boltzmann Machine Learning
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with in...
Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de ...
PAMI
2007
156views more  PAMI 2007»
13 years 8 months ago
Selection and Fusion of Color Models for Image Feature Detection
—The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model indu...
Harro M. G. Stokman, Theo Gevers
COST
2007
Springer
118views Multimedia» more  COST 2007»
14 years 2 months ago
The Organization of a Neurocomputational Control Model for Articulatory Speech Synthesis
The organization of a computational control model of articulatory speech synthesis is outlined in this paper. The model is based on general principles of neurophysiology and cognit...
Bernd J. Kröger, Anja Lowit, Ralph Schnitker
ICCV
2005
IEEE
14 years 10 months ago
Combining Generative Models and Fisher Kernels for Object Recognition
Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in...
Alex Holub, Max Welling, Pietro Perona
JMLR
2010
134views more  JMLR 2010»
13 years 3 months ago
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Jan Lemeire, Kris Steenhaut