Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
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
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The Complexity of Learning According to Two Models of a Drifting Environment
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Linear Programming Boosting via Column Generation
Machine Learning
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Density Connected Clustering with Local Subspace Preferences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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We investigate a method to find local clusters in low dimensional subspaces of high dimensional data, e.g. in high dimensional image descriptions. Using cluster centers instead of the full set of data will speed up the performance of learning algorithms for object recognition, and might also improve performance because overfitting is avoided. Using the Graz01 database, our method outperforms a current standard method for feature extraction from high dimensional image representations.