Applied multivariate statistical analysis
Applied multivariate statistical analysis
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On Visualization and Aggregation of Nearest Neighbor Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive mixtures of local experts
Neural Computation
Classification Based on Hybridization of Parametric and Nonparametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Issues in stacked generalization
Journal of Artificial Intelligence Research
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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Traditional parametric and nonparametric classifiers used for statistical pattern recognition have their own strengths and limitations. While parametric methods assume some specific parametric models for density functions or posterior probabilities of competing classes, nonparametric methods are free from such assumptions. So, when these model assumptions are correct, parametric methods outperform nonparametric classifiers, especially when the training sample is small. But, violations of these assumptions often lead to poor performance by parametric classifiers, where nonparametric methods work well. In this article, we make an attempt to overcome these limitations of parametric and nonparametric approaches and combine their strengths. The resulting classifiers, denoted the hybrid classifiers, perform like parametric classifiers when the model assumptions are valid, but unlike parametric classifiers, they also provide safeguards against possible deviations from parametric model assumptions. In this article, we propose some multiscale methods for hybrid classification, and their performance is evaluated using several simulated and benchmark data sets.