On the boosting ability of top-down decision tree learning algorithms

Proceedings of the Annual ACM Symposium on Theory of Computing

Volumn Part F129452, Issue , 1996, Pages 459-468

  Kearns, Michael ^a   Mansour, Yishay ^b  

^a AT AND T LABS RESEARCH   (United States)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DATA MINING; DECISION TREES; ERRORS; APPROXIMATION THEORY; COMPUTER SOFTWARE; DATA STRUCTURES; FUNCTIONS; HEURISTIC METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; POLYNOMIALS; RANDOM PROCESSES;

BOOSTING ALGORITHM; DECISION TREE LEARNING; DECISION TREE LEARNING ALGORITHM; FIRST PRINCIPLES; PRIMARY CONTRIBUTION; SPLITTING CRITERION; THEORETICAL MODELING; TOP DOWN ALGORITHM; BOOSTING ALGORITHMS; DECISION TREE; RANDOM GUESSING; TARGET FUNCTION; TOP DOWN ALGORITHMS; WEAK LEARNING;

LEARNING ALGORITHMS;

EID: 0029700730     PISSN: 07378017     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/237814.237994     Document Type: Conference Paper

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