DocumentCode
3256525
Title
Supervised learning with artificial selection
Author
Hagiwara, Manabu ; Nakagawa, Masaki
Author_Institution
Fac. of Sci. & Technol., Keio Univ., Yokohama, Japan
fYear
1989
fDate
0-0 1989
Abstract
Summary form only given, as follows. Supervised learning with artificial selection is proposed as a way to escape from local minima. The concept of artificial selection is reasonable for nature. In the authors´ method, the ´worst´ hidden unit is detected, and then all the weights connected to the detected hidden unit are reset to small random values. According to simulations, only half the trials using conventional backpropagation converge, whereas all of the trials using the proposed method converge, and quickly do so.<>
Keywords
convergence of numerical methods; digital simulation; learning systems; neural nets; backpropagation; convergence; escape from local minima; simulations; supervised learning with artificial selections; Convergence of numerical methods; Learning systems; Neural networks; Simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location
Washington, DC, USA
Type
conf
DOI
10.1109/IJCNN.1989.118443
Filename
118443
Link To Document