摘要
The separation of patterns from ground is the necessary requirement for recognition. Most of neural recognition models are network-centered without the ability to extract patterns. As a result, some non-neural methods and the learning of pattern's variant positions are used to complete the task. This article presents a spiking double-conversion network (DCN) to search for patterns in input using the double conversions from the network-centered input vector to a time sequence and further from the sequence to pattern-centered vector. DCN is designed for network-centered recognition and cluster models to extend them to world-centered ones.
源语言 | 英语 |
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页(从-至) | 173-183 |
页数 | 11 |
期刊 | Neurocomputing |
卷 | 30 |
期 | 1-4 |
DOI | |
出版状态 | 已出版 - 2000 |