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PKDD
2009
Springer
124views Data Mining» more  PKDD 2009»
13 years 11 months ago
Capacity Control for Partially Ordered Feature Sets
Abstract. Partially ordered feature sets appear naturally in many classification settings with structured input instances, for example, when the data instances are graphs and a fe...
Ulrich Rückert
PKDD
2009
Springer
196views Data Mining» more  PKDD 2009»
13 years 11 months ago
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective
We consider causally sufficient acyclic causal models in which the relationship among the variables is nonlinear while disturbances have linear effects, and show that three princi...
Kun Zhang, Aapo Hyvärinen
PKDD
2009
Springer
147views Data Mining» more  PKDD 2009»
13 years 11 months ago
Kernel Polytope Faces Pursuit
Abstract. Polytope Faces Pursuit (PFP) is a greedy algorithm that approximates the sparse solutions recovered by 1 regularised least-squares (Lasso) [4,10] in a similar vein to (Or...
Tom Diethe, Zakria Hussain
PKDD
2009
Springer
108views Data Mining» more  PKDD 2009»
13 years 11 months ago
Taxonomy-Driven Lumping for Sequence Mining
Francesco Bonchi, Carlos Castillo, Debora Donato, ...
PKDD
2009
Springer
102views Data Mining» more  PKDD 2009»
13 years 11 months ago
Are We There Yet?
Nello Cristianini
PKDD
2009
Springer
92views Data Mining» more  PKDD 2009»
13 years 11 months ago
A Generic Approach to Topic Models
This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a represent...
Gregor Heinrich
PKDD
2009
Springer
174views Data Mining» more  PKDD 2009»
13 years 11 months ago
Active and Semi-supervised Data Domain Description
Data domain description techniques aim at deriving concise descriptions of objects belonging to a category of interest. For instance, the support vector domain description (SVDD) l...
Nico Görnitz, Marius Kloft, Ulf Brefeld
PKDD
2009
Springer
102views Data Mining» more  PKDD 2009»
13 years 11 months ago
Relevance Grounding for Planning in Relational Domains
Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
Tobias Lang, Marc Toussaint
PKDD
2009
Springer
112views Data Mining» more  PKDD 2009»
13 years 11 months ago
Max-Margin Weight Learning for Markov Logic Networks
Tuyen N. Huynh, Raymond J. Mooney
PKDD
2009
Springer
129views Data Mining» more  PKDD 2009»
13 years 11 months ago
Considering Unseen States as Impossible in Factored Reinforcement Learning
Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a ...
Olga Kozlova, Olivier Sigaud, Pierre-Henri Wuillem...