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IJAR
2006
89views more  IJAR 2006»
13 years 7 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
EDBT
2000
ACM
13 years 11 months ago
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fixed size. The Slim-tree uses the "fat-factor" which provides a simple ...
Caetano Traina Jr., Agma J. M. Traina, Bernhard Se...
ICML
2004
IEEE
14 years 8 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
KDD
2012
ACM
166views Data Mining» more  KDD 2012»
11 years 10 months ago
Selecting a characteristic set of reviews
Online reviews provide consumers with valuable information that guides their decisions on a variety of fronts: from entertainment and shopping to medical services. Although the pr...
Theodoros Lappas, Mark Crovella, Evimaria Terzi
KDD
2009
ACM
239views Data Mining» more  KDD 2009»
14 years 8 months ago
Tell me something I don't know: randomization strategies for iterative data mining
There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however...
Heikki Mannila, Kai Puolamäki, Markus Ojala, ...