Titlebook: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003; Joint International Okyay Kaynak,Ethem Alpaydin,Lei Xu C

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Selective Sampling Methods in One-Class Classification Problemse most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the most informative examples in the context of one-class

DEFT

Learning Distributed Representations of High-Arity Relational Data with Non-linear Relational Embeddrepresent any binary relations, but that there are relations of arity greater than 2 that it cannot represent. We then introduce Non-Linear Relational Embedding (NLRE) and show that it can learn any relation. Results of NLRE on the Family Tree Problem show that generalization is much better than the

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addict

Felipe Yera Barchi,Fabiana Lopes da Cunhasional stochastic Hopfield networks. For these Hidden Hopfield Models (HHMs), mean field methods are derived for learning discrete and continuous temporal sequences. We also discuss applications of HHMs to learning of incomplete sequences and reconstruction of 3D occupancy graphs.

BARB

多嘴多舌

Approximate Learning in Temporal Hidden Hopfield Modelssional stochastic Hopfield networks. For these Hidden Hopfield Models (HHMs), mean field methods are derived for learning discrete and continuous temporal sequences. We also discuss applications of HHMs to learning of incomplete sequences and reconstruction of 3D occupancy graphs.

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0302-9743 ent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics..978-3-540-40408-8978-3-540-44989-8Series ISSN 0302-9743 Series E-ISSN 1611-3349