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AIPS
2008
13 years 11 months ago
Learning Relational Decision Trees for Guiding Heuristic Planning
The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computat...
Tomás de la Rosa, Sergio Jiménez, Da...
PR
2008
79views more  PR 2008»
13 years 8 months ago
Region-based image retrieval with high-level semantics using decision tree learning
Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval system with high-level semantic learning. The key f...
Ying Liu, Dengsheng Zhang, Guojun Lu
VLDB
1998
ACM
120views Database» more  VLDB 1998»
14 years 1 months ago
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each c...
Rajeev Rastogi, Kyuseok Shim
BMCBI
2010
142views more  BMCBI 2010»
13 years 9 months ago
pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree
Background: Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-ba...
Frederick A. Matsen III, Robin B. Kodner, E. Virgi...
ECCV
2010
Springer
13 years 9 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof