Genomic and proteomic sequence recognition using a connectionist inference model.
Bavan, A. S. and 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
Full text is not in this repository.
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.
|Research Areas:||A. > School of Science and Technology > Computer and Communications Engineering|
|Depositing User:||Repository team|
|Date Deposited:||18 May 2009 15:21|
|Last Modified:||15 Feb 2016 15:06|
Actions (login required)