TY - GEN
T1 - Integrated model for informal inference based on neural networks
AU - Kim, Kyung Joong
AU - Cho, Sung Bae
PY - 2008
Y1 - 2008
N2 - Inference is one of human's high-level functionalities and it is not easy to implement in machine. It is believed that inference is not results of single neuron's activity. Instead, it is a complex activity generated by multiple neural networks. Unlike computer, it is more flexible and concludes differently even for the similar situations in case of human. In this paper, these characteristics are defined as "informality." Informality in inference can be implemented using the interaction of multiple neural networks with the inclusion of internal or subjective properties. Simple inference tasks such as pattern recognition and robot control are solved based on the informal inference ideas. Especially, fuzzy integral and behavior network methods are adopted to realize that. Experimental results show that the informal inference can perform better with more flexibility compared to the previous static approaches.
AB - Inference is one of human's high-level functionalities and it is not easy to implement in machine. It is believed that inference is not results of single neuron's activity. Instead, it is a complex activity generated by multiple neural networks. Unlike computer, it is more flexible and concludes differently even for the similar situations in case of human. In this paper, these characteristics are defined as "informality." Informality in inference can be implemented using the interaction of multiple neural networks with the inclusion of internal or subjective properties. Simple inference tasks such as pattern recognition and robot control are solved based on the informal inference ideas. Especially, fuzzy integral and behavior network methods are adopted to realize that. Experimental results show that the informal inference can perform better with more flexibility compared to the previous static approaches.
UR - http://www.scopus.com/inward/record.url?scp=54049114339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=54049114339&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69162-4_99
DO - 10.1007/978-3-540-69162-4_99
M3 - Conference contribution
AN - SCOPUS:54049114339
SN - 3540691596
SN - 9783540691594
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 950
EP - 959
BT - Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
T2 - 14th International Conference on Neural Information Processing, ICONIP 2007
Y2 - 13 November 2007 through 16 November 2007
ER -