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» Convergence analysis of convex incremental neural networks
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MP
2010
162views more  MP 2010»
13 years 7 months ago
Approximation accuracy, gradient methods, and error bound for structured convex optimization
Convex optimization problems arising in applications, possibly as approximations of intractable problems, are often structured and large scale. When the data are noisy, it is of i...
Paul Tseng
NN
2006
Springer
108views Neural Networks» more  NN 2006»
13 years 8 months ago
Performance analysis of LVQ algorithms: A statistical physics approach
Learning vector quantization (LVQ) constitutes a powerful and intuitive method for adaptive nearest prototype classification. However, original LVQ has been introduced based on he...
Anarta Ghosh, Michael Biehl, Barbara Hammer
AAAI
1996
13 years 10 months ago
Constructive Neural Network Learning Algorithms
Constructive learning algorithms offer an attractive approach for the incremental construction of near-minimal neural-network architectures for pattern classification. They help ov...
Rajesh Parekh, Jihoon Yang, Vasant Honavar
AIIA
2001
Springer
14 years 1 months ago
Wide Coverage Incremental Parsing by Learning Attachment Preferences
This paper presents a novel method for wide coverage parsing using an incremental strategy, which is psycholinguistically motivated. A recursive neural network is trained on treeba...
Fabrizio Costa, Vincenzo Lombardo, Paolo Frasconi,...
ISNN
2005
Springer
14 years 2 months ago
One-Bit-Matching ICA Theorem, Convex-Concave Programming, and Combinatorial Optimization
Recently, a mathematical proof is obtained in (Liu, Chiu, Xu, 2004) on the so called one-bit-matching conjecture that all the sources can be separated as long as there is an one-to...
Lei Xu