Neocognitron trained by winner-kill-loser with triple threshold

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7063 LNCS, Issue PART 2, 2011, Pages 628-637

  Fukushima, Kunihiko ^a,b   Hayashi, Isao ^b   Léveillé, Jasmin ^c  

^a FUZZY LOGIC SYSTEMS INSTITUTE   (Japan)

^b KANSAI UNIVERSITY   (Japan)

^c BOSTON UNIVERSITY   (United States)

Author keywords

Hierarchical network; Neocognitron; Triple threshold; Visual pattern recognition; Winner kill loser

Indexed keywords

HIERARCHICAL NETWORK; NEOCOGNITRON; TRIPLE THRESHOLD; VISUAL PATTERN RECOGNITION; WINNER-KILL-LOSER;

CHARACTER RECOGNITION; COMPUTER SIMULATION; DATA PROCESSING; NETWORK LAYERS;

FEEDFORWARD NEURAL NETWORKS;

EID: 81855218290     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24958-7_73     Document Type: Conference Paper

References (5)

1. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193-202 (1980)
2. Fukushima, K.: Neocognitron trained with winner-kill-loser rule. Neural Networks 23(7), 926-938 (2010)
3. Fukushima, K.: Neocognitron for handwritten digit recognition. Neurocomputing 51, 161-180 (2003)
4. Fukushima, K.: Increasing robustness against background noise: visual pattern recognition by a neocognitron. Neural Networks 24(7), 767-778 (2011)
5. ETL1 database, http://www.is.aist.go.jp/etlcdb/#English