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» Learning the Structure of Dynamic Probabilistic Networks
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IJCNN
2006
IEEE
14 years 1 months ago
On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning
— We present a simple, intuitive argument based on “invariant imbedding” in the spirit of dynamic programming to derive a stagewise second-order backpropagation (BP) algorith...
Eiji Mizutani, Stuart Dreyfus
ECCV
2002
Springer
14 years 9 months ago
Learning to Parse Pictures of People
The detection of people is one of the foremost problems for indexing, browsing and retrieval of video. The main difficulty is the large appearance variations caused by action, clot...
Rémi Ronfard, Cordelia Schmid, Bill Triggs
ICDAR
2007
IEEE
14 years 1 months ago
Energy-Based Models in Document Recognition and Computer Vision
The Machine Learning and Pattern Recognition communities are facing two challenges: solving the normalization problem, and solving the deep learning problem. The normalization pro...
Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato, Fu...
IJCNN
2007
IEEE
14 years 1 months ago
Integrating a Flexible Representation Machinery in a Model of Human Concept Learning
— High-order human cognition involves processing of abstract and categorically represented knowledge. Traditionally, it has been considered that there is a single innate internal...
Toshihiko Matsuka, Yasuaki Sakamoto
CORR
2011
Springer
174views Education» more  CORR 2011»
12 years 11 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato