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» Unsupervised learning in neural computation
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ICANN
2009
Springer
14 years 1 months ago
Switching Hidden Markov Models for Learning of Motion Patterns in Videos
Abstract. Building on the current understanding of neural architecture of the visual cortex, we present a graphical model for learning and classification of motion patterns in vid...
Matthias Höffken, Daniel Oberhoff, Marina Kol...
TNN
2010
143views Management» more  TNN 2010»
13 years 3 months ago
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
SAC
2005
ACM
14 years 2 months ago
A hierarchical naive Bayes mixture model for name disambiguation in author citations
Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web ...
Hui Han, Wei Xu, Hongyuan Zha, C. Lee Giles
NIPS
2003
13 years 10 months ago
Training a Quantum Neural Network
Most proposals for quantum neural networks have skipped over the problem of how to train the networks. The mechanics of quantum computing are different enough from classical compu...
Bob Ricks, Dan Ventura
CVPR
2008
IEEE
14 years 10 months ago
Parameterized Kernel Principal Component Analysis: Theory and applications to supervised and unsupervised image alignment
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
Fernando De la Torre, Minh Hoai Nguyen