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Autoassociative memory, also known as auto-association memory or an autoassociation network, is often misunderstood to be only a form of backpropagation or other neural networks. It is actually a more generic term that refers to all memories that enable one to retrieve a piece of data from only a tiny sample of itself. Traditional memory stores data at a unique address and can ''recall'' the data upon presentation of the complete unique address. Autoassociative memories are capable of retrieving a piece of data upon presentation of only partial information from ''that'' piece of data. Heteroassociative memories, on the other hand, can recall an associated piece of datum from ''one'' category upon presentation of data from ''another'' category. Hopfield networks 〔()〕 have been shown 〔(Artificial Intelligence Illuminated - Ben Coppin - Google Books ). Books.google.co.uk. Retrieved on 2013-11-20.〕 to act as autoassociative memory since they are capable of remembering data by observing a portion of that data. Bidirectional Associative Memories (BAM) 〔http://sipi.usc.edu/~kosko/BAM.pdf〕 are Artificial Neural Networks that have long been used for performing heteroassociative recall. For example, the fragments presented below should be all that's necessary to retrieve the appropriate memory: #"To be or not to be, that is _____" #"I came, I saw, _____" Readers will be able to complete the phrases above, given only a portion. The conclusion to be drawn is that Autoassociation networks can recreate the whole from merely its small parts. ==See also== * Bidirectional Associative Memory 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Autoassociative memory」の詳細全文を読む スポンサード リンク
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