Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Backpropagation: the basic theory
Backpropagation
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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Performance analysis of connectionist paradigms for modeling chaotic behavior of stock indices
Second international workshop on Intelligent systems design and application
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
Integrating Ensemble of Intelligent Systems for Modeling Stock Indices
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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A new classifier based on Bayes' principle that assumes theclustering of attribute values while boosting the attributedifferences is presented. The method considers the error producedby each example in the training set in turn and updates theconnection weights associated to the probability P(U_{m}\mid C_{k})of each attribute of that example. In this process the probabilitydensity of identical attribute values flattens out and thedifferences get boosted up. Using four popular datasets from theUCI repository, some of the characteristic features of the networkare illustrated. The network is found to have optimalgeneralization ability on all the datasets. For a given topology,the network converges to the same classification accuracy and thetraining time as compared to other networks is less. One of theexamples indicates the possibility that the optimization of thenetwork may be done in parallel.