ML
2002
ACM
13 years 11 months ago
2002
ACM
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as...
ML
2002
ACM
13 years 11 months ago
2002
ACM
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated...
ML
2002
ACM
13 years 11 months ago
2002
ACM
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...
ML
2002
ACM
13 years 11 months ago
2002
ACM
When simple parametric models such as linear regression fail to adequately approximate a relationship across an entire set of data, an alternative may be to consider a partition o...
ML
2002
ACM
13 years 11 months ago
2002
ACM
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the gener...
ML
2002
ACM
13 years 11 months ago
2002
ACM
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitt...
ML
2002
ACM
13 years 11 months ago
2002
ACM
Binningandtruncationofdataarecommonindataanalysisandmachinelearning.Thispaperaddresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM ap...
ML
2002
ACM
13 years 11 months ago
2002
ACM
For two-class datasets, we provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned ...
ML
2002
ACM
13 years 11 months ago
2002
ACM
Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while t...
ML
2002
ACM
13 years 11 months ago
2002
ACM
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...