An introduction to neural computing
An introduction to neural computing
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
N-Tuple Features for OCR Revisited
IEEE Transactions on Pattern Analysis and Machine Intelligence
RAM-Based Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Weighted Ensemble Classifiers - A Modified Decision Scheme
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Criticality dispersion in swarms to optimize n-tuples
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybridisation of GA and PSO to optimise N-tuples
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
| Hi-index | 0.14 |
The anticipated behavior of the n-tuple classification system is that it gives the highest output score for the class to which the input example actually belongs. By performing a theoretical analysis of how the output scores are related to the underlying probability distributions of the data, this paper shows that this in general is not to be expected. The theoretical results are able to explain the behavior that is observed in experimental studies. The theoretical analysis also give valuable insight into how the n-tuple classifier can be improved to deal with skewed training priors, which until now have been a hard problem for the architecture to tackle. It is shown that by relating an output score to the probability that a given class generates the data makes it possible to design the n-tuple net to operate as a close approximation to the Bayes estimator. It is specifically illustrated that this approximation can be obtained by modifying the decision criteria. In real cases, the underlying example distributions are unknown and accordingly the optimum way to treat the output scores cannot be calculated theoretically. However, it is shown that the feasibility of performing leave-one-out cross-validation tests in n-tuple networks makes it possible to obtain proper processing of the scores in such cases.