The Strength of Weak Learnability
Machine Learning
Connectionist learning procedures
Machine learning: paradigms and methods
Skeletonization: a technique for trimming the fat from a network via relevance assessment
Advances in neural information processing systems 1
Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Processing issues in comparisons of symbolic and connectionist learning systems
Proceedings of the sixth international workshop on Machine learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Advances in neural information processing systems 2
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Methods to speed up error back-propagation learning algorithm
ACM Computing Surveys (CSUR)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Initializing RBF-networks with small subsets of training examples
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
International Workshop on Combinations of Genetic Algorithms and Neural Networks
International Workshop on Combinations of Genetic Algorithms and Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
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An appropriately designed architecture of a neural network is essential to many realistic pattern-recognition tasks. A choice of just the right number of neurons, and their interconnections, can cut learning costs by orders of magnitude, and still warrant high classification accuracy. Surprisingly, textbooks often neglect this issue. A specialist seeking systematic information will soon realize that relevant material is scattered over diverse sources, each with a different perspective, terminology and goals. This brief survey attempts to rectify the situation by explaining the involved aspects, and by describing some of the fundamental techniques.