Perceptrons: expanded edition
A new approach to classification of brainwaves
Pattern Recognition
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Learning of modular structured networks
Artificial Intelligence - Special issue: AI research in Japan
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Generalization properties of modular networks: implementing the parity function
IEEE Transactions on Neural Networks
Practical linear space algorithms for computing string-edit distances
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Hi-index | 0.00 |
The even-odd parity problem is a tough one for neural networks to handle because they assume a finite dimensional vector space. Typically, the size of the neural network increases as the size of the problem increases. The triple parity problem is even tougher. In this paper, a method is proposed for supervised and unsupervised learning to classify bit strings of arbitrary length in terms of their triple parity: The learner is modeled by two formal concepts, transformation system and stability optimization. Even though a small set of short examples were used in the training stage, all bit strings of any length were classified correctly in the online recognition stage. The proposed learner has successfully learned to devise a way by means of metric calculations to classify bit strings of any length according to their triple parity. The system was able to acquire the concept of counting, dividing, and then taking the remainder, by autonomously evolving a set of string-editing rules along with their appropriate weights to solve the difficult problem.