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» Learning Generative Models via Discriminative Approaches
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117
Voted
ICASSP
2010
IEEE
14 years 11 months ago
Multiple sequence alignment based bootstrapping for improved incremental word learning
We investigate incremental word learning with few training examples in a Hidden Markov Model (HMM) framework suitable for an interactive learning scenario with little prior knowle...
Irene Ayllól Clemente, Martin Heckmann, Ger...
101
Voted
ML
1998
ACM
102views Machine Learning» more  ML 1998»
15 years 6 days ago
Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach
We review the application of statistical mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward netwo...
Renato Vicente, Osame Kinouchi, Nestor Caticha
103
Voted
CIKM
1997
Springer
15 years 4 months ago
Learning Belief Networks from Data: An Information Theory Based Approach
This paper presents an efficient algorithm for learning Bayesian belief networks from databases. The algorithm takes a database as input and constructs the belief network structur...
Jie Cheng, David A. Bell, Weiru Liu
CVPR
1998
IEEE
16 years 2 months ago
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
We describe a technique for using the joint occurrence of local features at multiple resolutions to measure the similarity between texture images. Though superficially similar to ...
Jeremy S. De Bonet, Paul A. Viola
CVPR
2006
IEEE
16 years 2 months ago
Using Dependent Regions for Object Categorization in a Generative Framework
"Bag of words" models have enjoyed much attention and achieved good performances in recent studies of object categorization. In most of these works, local patches are mo...
Gang Wang, Ye Zhang, Fei-Fei Li 0002