Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Neurocomputing
Neural network architectures: an introduction
Neural network architectures: an introduction
Artificial neural systems: foundations, paradigms, applications, and implementations
Artificial neural systems: foundations, paradigms, applications, and implementations
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Advances in neural information processing systems 2
Machine Learning - Special issue on applications in molecular biology
Flexible case-based retrieval for comparative genomics
Applied Intelligence
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A modified counter-propagation (CP) algorithm with supervised learning vector quantizer (LVQ) and dynamic node allocation has been developedfor rapid classification of molecular sequences. The molecular sequences were encoded into neural input vectors using an n-gram hashing method for word extraction and a singular value decomposition (SVD) method for vector compression. The neural networks used were three-layered, forward-only CP networks that performed nearest neighbor classification. Several factors affecting the CP performance were evaluated, including weight initialization, Kohonen layer dimensioning, winner selection and weight update mechanisms. The performance of the modified CP network was compared with the back-propagation (BP) neural network and the k-nearest neighbor method. The major advantages of the CP network are its training and classification speed and its capability to extract statistical properties of the input data. The combined BP and CP networks can classify nucleic acid or protein sequences with a close to 100% accuracy at a rate of about one order of magnitude faster than other currently available methods.