PKDD
2009
Springer
14 years 3 months ago
2009
Springer
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
Subgroup discovery aims at finding subsets of a population whose class distribution is significantly different from the overall distribution. It has previously predominantly been...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an e...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
The goal of approximate policy evaluation is to “best” represent a target value function according to a specific criterion. Temporal difference methods and Bellman residual m...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. ...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...
PKDD
2009
Springer
14 years 3 months ago
2009
Springer
Subgroup discovery is a local pattern discovery task, in which descriptions of subpopulations of a database are evaluated against some quality function. As standard quality functio...