Genomic and proteomic sequence recognition using a connectionist inference model.
Bavan, A. S., Ford, Martyn and Kalatzi, Melina (2000) Genomic and proteomic sequence recognition using a connectionist inference model. Journal of chemical technology and biotechnology, 75 (10) . pp. 901-912. ISSN 0264-3413 [Article] (doi:10.1002/1097-4660(200010))
Abstract
In this paper a proposal for implementing a connectionist associative memory model (CAMM) based on a novel approach for recognising sequences is presented. The objective of the CAMM is to satisfy medium-high capacity and the retrieval of an arbitrary number of multiple associative memories that satisfy the stimulus input. The architecture is constructed on-the-fly and is dependent on the information in the training set. The model is composed of two stages; StageI and StageII. StageI is concerned with the development of a state space graph representing the training set and embedding that graph in a connectionist model. During retrieval a graph is produced that represents the candidate solutions; some spurious memories may infiltrate the solution space which is removed in StageII using conventional techniques.
| Item Type: | Article |
|---|---|
| Research Areas: | A. > School of Science and Technology > Computer and Communications Engineering |
| Item ID: | 2335 |
| Useful Links: | |
| Depositing User: | Repository team |
| Date Deposited: | 18 May 2009 15:21 |
| Last Modified: | 13 Oct 2016 14:14 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/2335 |
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