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» Generating Predicate Rules from Neural Networks
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JMLR
2008
188views more  JMLR 2008»
13 years 7 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
ESANN
2008
13 years 9 months ago
Word recognition and incremental learning based on neural associative memories and hidden Markov models
Abstract. An architecture for achieving word recognition and incremental learning of new words in a language processing system is presented. The architecture is based on neural ass...
Zöhre Kara Kayikci, Günther Palm
IJON
2007
184views more  IJON 2007»
13 years 7 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
CADE
2000
Springer
14 years 1 days ago
Machine Instruction Syntax and Semantics in Higher Order Logic
Abstract. Proof-carrying code and other applications in computer security require machine-checkable proofs of properties of machine-language programs. These in turn require axioms ...
Neophytos G. Michael, Andrew W. Appel
GECCO
2005
Springer
141views Optimization» more  GECCO 2005»
14 years 1 months ago
Constructing good learners using evolved pattern generators
Self-organization of brain areas in animals begins prenatally, evidently driven by spontaneously generated internal patterns. The neural structures continue to develop postnatally...
Vinod K. Valsalam, James A. Bednar, Risto Miikkula...