The cascade-correlation learning architecture
Advances in neural information processing systems 2
An adaptive version of the boost by majority algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Machine Learning
A robust minimax approach to classification
The Journal of Machine Learning Research
Is nonparametric learning practical in very high dimensional spaces?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
| Hi-index | 0.00 |
A new class of nonparametric algorithms for high-dimensional binary classification is proposed using cascades of low dimensional polynomial structures. Construction of polynomial cascades is based on Minimax Probability Machine Classification (MPMC), which results in direct estimates of classification accuracy, and provides a simple stopping criteria that does not require expensive cross-validation measures. This Polynomial MPMC Cascade (PMC) algorithm is constructed in linear time with respect to the input space dimensionality, and linear time in the number of examples, making it a potentially attractive alternative to algorithms like support vector machines and standard MPMC. Experimental evidence is given showing that, compared to state-of-the-art classifiers, PMCs are competitive; inherently fast to compute; not prone to overfitting; and generally yield accurate estimates of the maximum error rate on unseen data.