Presentation 1996/3/19
A self-supervised learning system for category detection by sensory integration
Koichiro YAMAUCHI, Mikiya OOTA, Naohiro ISHII,
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Abstract(in English) Artificial neural network is a useful tool for pattern recognition because the network can realize nonlinear mapping between input and output spaces. This ability is tuned by supervised learning methods such as back-propagation. In the supervised learning methods, desired outputs of the neural network are needed. However, the desired outputs are usually unknown in unpredictable environments. To solve this problem, this paper presents a self-supervised learning system for category detection. This system learns categories of objects and boundaries between them automatically by integrating information from several sensors. We assume that these sensory inputs are always ambiguous patterns which include some noises according to deformation of the objects. After the learning, the system recognizes objects with controlling a priority of each sensor according to the deformation of the sensory input pattern.
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Keyword(in English) Neural Network / Self-Supervised-learning / Back-Propagation / Sensory Fusion
Paper # NC95-160
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Committee NC
Conference Date 1996/3/19(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A self-supervised learning system for category detection by sensory integration
Sub Title (in English)
Keyword(1) Neural Network
Keyword(2) Self-Supervised-learning
Keyword(3) Back-Propagation
Keyword(4) Sensory Fusion
1st Author's Name Koichiro YAMAUCHI
1st Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology()
2nd Author's Name Mikiya OOTA
2nd Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology
3rd Author's Name Naohiro ISHII
3rd Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology
Date 1996/3/19
Paper # NC95-160
Volume (vol) vol.95
Number (no) 599
Page pp.pp.-
#Pages 8
Date of Issue