Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Handbook of Neural Computation
Handbook of Neural Computation
Comparison of plasticity of self-optimizing neural networks and natural neural networks
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
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Many times we come across the need to simplify or reduce an input data space in order to achieve a better model or better performance of an artificial intelligence solution. The well known PCA, ICA and rough sets can simplify and reduce input data space but they cannot transform real input data vectors into binary ones. Binary training vectors can simplify a training process of neural networks and let them to construct more compact topologies. This paper introduces a new algorithm that reduces input data space and simultaneously automatically lossy transforms real input training data vectors into binary vectors so that they do not lose their discrimination properties. The problem is how to effectively transform real input training data vectors into binary vectors so that an input data space could be simplified and the transformed binary vectors would be enough representative to be able to discriminate all training samples of all classes correctly? The described lossy conversion makes possible to achieve better generalization results for various soft-computing algorithms, can be widely used and avoids the curse of dimensionality problem. This paper introduces a new Automatic Discriminative Lossy Binary Conversion Algorithm (ADLBCA) that is able to solve all these tasks. Generally, no other method can simultaneously and so fast do all these tasks.