A study of neural-network-based classifiers for material classification

Neurocomputing

Volumn 144, Issue , 2014, Pages 367-377

  Lam, H K ^a   Ekong, Udeme ^a   Liu, Hongbin ^a   Xiao, Bo ^a   Araujo, Hugo ^a   Ling, Sai Ho ^b   Chan, Kit Yan ^c  

^a KING'S COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c CURTIN UNIVERSITY   (Australia)

Author keywords

Classifier; Material classification; Neural networks

Indexed keywords

BINARY TREES; CLASSIFIERS; FEATURE EXTRACTION; FORESTRY; NEURAL NETWORKS; SPAMMING;

FEATURE EXTRACTION METHODS; GAUSSIAN WHITE NOISE; INDIVIDUAL OBJECTS; K-NEAREST NEIGHBOR CLASSIFIER; MATERIAL CLASSIFICATION; NAIVE BAYES CLASSIFIERS; RECOGNITION ACCURACY; ROBUSTNESS PROPERTIES;

TREES (MATHEMATICS);

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CLASSIFICATION; CLASSIFIER; DAILY LIFE ACTIVITY; DATA EXTRACTION; K NEAREST NEIGHBOR; MATERIALS; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROBLEM SOLVING; RECOGNITION; RESEARCH; STATISTICAL PARAMETERS; SURFACE PROPERTY;

CLASSIFICATION; CLASSIFIERS; FORESTRY; MATERIALS; MATHEMATICS; NEURAL NETWORKS; TREES;

EID: 84905992241     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.05.019     Document Type: Article

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