Learning Bayesian networks by hill climbing: Efficient methods based on progressive restriction of the neighborhood

Data Mining and Knowledge Discovery

Volumn 22, Issue 1-2, 2011, Pages 106-148

  Gámez, José A ^a   Mateo, Juan L ^a   Puerta, José M ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CANDIDATE SOLUTION; COMPUTATIONAL DEMANDS; COMPUTATIONALLY EFFICIENT; DATA SETS; DIFFERENT DOMAINS; EFFICIENT METHOD; HEURISTIC SEARCH; HIGH-DIMENSIONAL; HILL CLIMBING; HILL CLIMBING ALGORITHMS; LEARNING APPROACH; LEARNING BAYESIAN NETWORKS; NP-HARD PROBLEM; OPTIMAL RESULTS; SEARCH PROCESS; THEORETICAL RESULT; TRAINING DATA; TWO STAGE;

ALGORITHMS; BEHAVIORAL RESEARCH; COMPUTATIONAL COMPLEXITY; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; PROBABILITY DISTRIBUTIONS;

BAYESIAN NETWORKS;

EID: 78651369196     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-010-0178-6     Document Type: Article

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