Searching for "Learning Decision Tree Classifiers." – sorted by Relevance.
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Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data
- Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data
- Cited by 12 (7 self) – Add To MetaCart
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Extraposition: A case study in German sentence realization
- approaches to the modeling of extraposition, both based on machine learned decision tree classifiers. The two
- Cited by 2 (1 self) – Add To MetaCart
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Bagging, Boosting, and C4.5
- of learning decision tree classifiers, examples of methods that improve accuracy are: ffl Construction
- Cited by 222 (1 self) – Add To MetaCart
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Improved Use of Continuous Attributes in C4.5
- that learns decision-tree classifiers. Several authors have recently noted that C4.5's performance is weaker
- Cited by 130 (1 self) – Add To MetaCart
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Improved Use of Continuous Attributes in C4.5
- that learns decision-tree classifiers. Several authors have recently noted that C4.5's performance is weaker
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Algorithms and software for collaborative discovery from autonomous, semantically heterogeneous, distributed information sources
- provably exact algorithms for learning Naive Bayes, Nearest Neighbor, and Decision Tree classifiers from
- Cited by 9 (7 self) – Add To MetaCart
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Learning to Tag Multilingual Texts Through Observation
- Tag learns decision tree classifiers that predict where tags of each type should begin and end in the text
- Cited by 21 (1 self) – Add To MetaCart
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A Decision Tree Plug-In for DataEngine
- learned decision tree stored in a file and a table of classified sample cases, which may or may
- Cited by 6 (1 self) – Add To MetaCart
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Center for Computational Intelligence, Learning, and Discovery About the Instructors
- , and Discovery Learning Decision Tree Classifiers • Decision trees are especially well suited for representing
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Towards Semantics-Enabled Infrastructure for Knowledge Acquisition from Distributed Data
- [19] Let L be a centralized algorithm for learning a decision tree classifier [Quinlan, 1993] h : X
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