The nature of statistical learning theory
The nature of statistical learning theory
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
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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Data projections have been used extensively to reduce input space dimensionality. Such reduction is useful to get faster results, and sometimes can help to discard unnecessary or noisy input dimensions. Random Projections (RP) can be computed faster than other methods as for example Principal Component Analysis (PCA). This paper presents an experimental study over 62 UCI datasets of three types of RPs taking into account the size of the projected space and using linear SVMs as base classifiers. We also combined random projections with sparse matrix strategy used by Rotation Forests, which is a method based in projections too. Results shows that Random Projections use to be better than using PCA for SVMs ensembles.