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» Lazy Learning for Improving Ranking of Decision Trees
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ICML
1996
IEEE
13 years 11 months ago
Second Tier for Decision Trees
A learner's performance does not rely only on the representation language and on the algorithm inducing a hypothesis in this language. Also the way the induced hypothesis is ...
Miroslav Kubat
JMLR
2008
111views more  JMLR 2008»
13 years 7 months ago
Ranking Categorical Features Using Generalization Properties
Feature ranking is a fundamental machine learning task with various applications, including feature selection and decision tree learning. We describe and analyze a new feature ran...
Sivan Sabato, Shai Shalev-Shwartz
ICML
2001
IEEE
14 years 8 months ago
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be mad...
Bianca Zadrozny, Charles Elkan
NAACL
2004
13 years 8 months ago
Detecting Structural Metadata with Decision Trees and Transformation-Based Learning
The regular occurrence of disfluencies is a distinguishing characteristic of spontaneous speech. Detecting and removing such disfluencies can substantially improve the usefulness ...
Joungbum Kim, Sarah E. Schwarm, Mari Ostendorf
PR
2008
79views more  PR 2008»
13 years 7 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