Sow-activity classification from acceleration patterns: A machine learning approach

Computers and Electronics in Agriculture

Volumn 93, Issue , 2013, Pages 17-26

  Escalante, Hugo Jair ^a   Rodriguez, Sara V ^b   Cordero, Jorge ^b   Kristensen, Anders Ringgaard ^c   Cornou, Cécile ^c  

^a INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

^b UNIVERSIDAD AUTÓNOMA DE NUEVO LEÓN   (Mexico)

^c UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Accelerometer measurements; CLOP; Logitboost with trees; Pattern classification; Sow activity classification

Indexed keywords

ACCELEROMETERS; ADAPTIVE BOOSTING; FORESTRY; LEARNING SYSTEMS; PATTERN RECOGNITION; TIME SERIES;

ACCELERATION PATTERN; ACCELEROMETER MEASUREMENT; BEST MODEL; CLASSIFICATION APPROACH; CLOP; DATA SET; ENSEMBLE METHODS; INDIVIDUAL PREDICTION; LOGITBOOST; MACHINE LEARNING APPROACHES; MULTI-CLASS; RECOGNITION RATES; RELATED WORKS; STANDARD MACHINES; SUPERVISED CLASSIFICATION; SUPERVISED LEARNING APPROACHES; TIMES SERIES; TREE-BASED;

CLASSIFICATION (OF INFORMATION);

ACCELEROMETER; ACTIVITY PATTERN; AGRICULTURAL MODELING; AGRICULTURAL TECHNOLOGY; ARTIFICIAL NEURAL NETWORK; EXPERIMENTAL STUDY; IMAGE CLASSIFICATION; PIG; TIME SERIES;

CLASSIFICATION; FORESTRY; INFORMATION RETRIEVAL;

EID: 84874450046     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2013.01.003     Document Type: Article

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