Abstract:
:We describe an analytical framework for the adaptations of neural systems that adapt its internal structure on the basis of subjective probabilities constructed by computation of randomly received input signals. A principled approach is provided with the key property that it defines a probability density model that allows studying the convergence of the adaptation process. In particular, the derived algorithm can be applied for approximation problems such as the estimation of probability densities or the recognition of regression functions. These approximation algorithms can be easily extended to higher-dimensional cases. Certain neural network models can be derived from our approach (e.g., topological feature maps and associative networks).
journal_name
Neural Computjournal_title
Neural computationauthors
Khaikine M,Holthausen Kdoi
10.1162/089976600300015862subject
Has Abstractpub_date
2000-02-01 00:00:00pages
433-50issue
2eissn
0899-7667issn
1530-888Xjournal_volume
12pub_type
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journal_title:Neural computation
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journal_title:Neural computation
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更新日期:2014-01-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
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journal_title:Neural computation
pub_type: 信件
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journal_title:Neural computation
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journal_title:Neural computation
pub_type: 杂志文章
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更新日期:2018-09-01 00:00:00
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journal_title:Neural computation
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journal_title:Neural computation
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journal_title:Neural computation
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journal_title:Neural computation
pub_type: 杂志文章,评审
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journal_title:Neural computation
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300014872
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journal_title:Neural computation
pub_type: 杂志文章
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journal_title:Neural computation
pub_type: 杂志文章
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journal_title:Neural computation
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更新日期:2011-05-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
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更新日期:2002-07-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
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journal_title:Neural computation
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