159 search results - page 11 / 32 » Incremental One-Class Learning with Bounded Computational Co... |
AUSAI
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. We present a new method for voting exponential (in the number of attributes) size sets of Bayesian classifiers in polynomial time with polynomial memory requirements. Tra...
ICPR
2002
IEEE
14 years 13 days ago
2002
IEEE
This paper describes a novel application of Statistical Learning Theory (SLT) for motion prediction. SLT provides analytical VC-generalization bounds for model selection; these bo...
FOCS
2010
IEEE
13 years 5 months ago
2010
IEEE
We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded ...
PODS
2012
ACM
11 years 10 months ago
2012
ACM
We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k...
COLT
1993
Springer
13 years 11 months ago
1993
Springer
The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis of learning problems in the PAC framework. For polynomial learnability, we seek upper bou...