神經網路很重要的一個特性是能由數據和經驗中學習。但是，數據很可能並不完整或不準確，情況也往往很不確定，而且多少也和經驗有點不同；如果只依據數據和經驗來做決定，很可能並不適用於新的場合。所以，應用概率來推論，有時有其必要性。因此，我們設計出一種所謂概率式漢明網路(probabilistic hamming net)，可以有效地學得概率分佈，並且還提供了明確的比對(explicit matching)和多工(multiplexing)的機構。對於提高判斷的準確性，應有很大的助益。 One of the main reasons for the popularity of neural networks lies in the ability to learn from data and/or experiences, which may compensate for what most expert systems might lack. However, since data may be incomplete and/or inaccurate and situations are usually uncertain and to some extent different from experiences, crisp decisions based on data or experiences are apt to fail in new situations. Therefore, the application of probabilistic inference seems indispensable in some circumstances. A probabilistic Hamming net is designed for discrete variables to effectively compute the probabilities and to provide explicit matching and multiplexing mechanisms.
