NIPS
2003
13 years 8 months ago
2003
There exist many different generalization error bounds for classification. Each of these bounds contains an improvement over the others for certain situations. Our goal is to com...
NIPS
2004
13 years 8 months ago
2004
We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margi...
NIPS
2004
13 years 8 months ago
2004
We prove generalization error bounds for predicting entries in a partially observed matrix by fitting the observed entries with a low-rank matrix. In justifying the analysis appro...
COLT
2005
Springer
14 years 26 days ago
2005
Springer
We study the rank, trace-norm and max-norm as complexity measures of matrices, focusing on the problem of fitting a matrix with matrices having low complexity. We present generali...
COLT
2005
Springer
14 years 26 days ago
2005
Springer
We present two new methods for obtaining generalization error bounds in a semi-supervised setting. Both methods are based on approximating the disagreement probability of pairs of ...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...