کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10326005 | 677468 | 2005 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improved local learning rule for information maximization and related applications
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
For a neural network comprising feedforward and lateral connections, a local learning rule is proposed that causes the lateral connections to learn directly the inverse of a covariance matrix. In contrast to earlier work, the rule involves just one processing pass through the lateral connections for each input presentation, and consists of a simple anti-Hebbian term. This provides an effective and simple method for online network learning algorithms that implement optimization principles, drawn from statistics or from information or control theory, for which a running estimate of the covariance matrix inverse is useful. An application to infomax learning (mutual information maximization) in the presence of input and output noise is used to illustrate the method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 18, Issue 3, April 2005, Pages 261-265
Journal: Neural Networks - Volume 18, Issue 3, April 2005, Pages 261-265
نویسندگان
Ralph Linsker,