FCANN: A new approach for extraction and representation of knowledge from ANN trained via Formal Concept Analysis

Neurocomputing

Volumn 71, Issue 13-15, 2008, Pages 2670-2684

  Zárate, Luis E ^a   Dias, S Mariano ^a   Song, M A Junho ^a  

^a PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Formal Concept Analysis; Machine learning; Rule extraction from neural networks

Indexed keywords

DEEP NEURAL NETWORKS; EXTRACTION; INFORMATION ANALYSIS; LEARNING SYSTEMS; NEURAL NETWORKS;

ADJUSTMENT FACTORS; FORMAL CONCEPT ANALYSES (FCA); IMPLICATION RULES; IMPLICIT KNOWLEDGE; OPERATIONAL CONDITIONS; PROCESS PARAMETERS; REAL-WORLD PROBLEM; RULE EXTRACTION FROM NEURAL NETWORKS;

FORMAL CONCEPT ANALYSIS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CASE STUDY; CONFERENCE PAPER; INTERMETHOD COMPARISON; KNOWLEDGE; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; SOLAR ENERGY; STATISTICAL DISTRIBUTION; VALIDATION PROCESS;

EID: 56449128898     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.09.025     Document Type: Conference Paper

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