Machine Learning
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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Random matrices in data analysis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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In this paper we propose and experimentally analyze ensemble methods based on random projections (as feature extraction method) and SVM with polynomial kernels (as learning algorithm). We show that, under suitable conditions, polynomial kernels are approximately preserved by random projections, with a degradation related to the square of the degree of the polynomial. Experimental results with Random Subspace and Random Projection ensembles of polynomial SVMs, support the hypothesis the low degree polynomial kernels, introducing with high probability lower distortions in the projected data, are better suited to the classification of high dimensional DNA microarray data.