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Titlebook: Innovations in ART Neural Networks; Lakhmi C. Jain,Beatrice Lazzerini,Ugur Halici Book 2000 Springer-Verlag Berlin Heidelberg 2000 Adaptiv

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發(fā)表于 2025-3-23 13:10:19 | 只看該作者
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發(fā)表于 2025-3-23 15:25:33 | 只看該作者
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發(fā)表于 2025-3-23 21:12:51 | 只看該作者
Incremental Learning and Optimization of Hierarchical Clusterings with Art-Based Modular Networks,patterns by self-organisation. The network is a cascade of Adaptive Resonance Theory (ART) modules, in which each module learns to cluster the . between the input pattern and the selected category prototype at the previous module. Input patterns are first classified into a few broad categories, and
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發(fā)表于 2025-3-24 00:56:23 | 只看該作者
,Art-Based Autonomous Learning Systems: Part I — Architectures and Algorithms,rpetuity. Fuzzy ARTMAP and the Probabilistic Neural Network are integrated to form a hybrid system that possesses the desirable properties for incremental, causal learning as well as for Bayesian probability estimation. Subsequently, a multiple neural network architecture is devised to aggregate out
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發(fā)表于 2025-3-24 06:08:28 | 只看該作者
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發(fā)表于 2025-3-24 17:34:34 | 只看該作者
M. Blume,D. A. Van Blerkomd imputation rules seems to be helpful. Evaluation rules give information as to whether a certain behavior is obligatory, forbidden or permitted. From these rules, the rules that give information as to whether or not a person can actually be held responsible for a certain behavior must be distinguis
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發(fā)表于 2025-3-24 21:01:18 | 只看該作者
A.-H. Tand imputation rules seems to be helpful. Evaluation rules give information as to whether a certain behavior is obligatory, forbidden or permitted. From these rules, the rules that give information as to whether or not a person can actually be held responsible for a certain behavior must be distinguis
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發(fā)表于 2025-3-24 23:33:40 | 只看該作者
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