Presentation 1999/5/20
A Neural Network with Trainable Nonlinear Connections and Activation Functions and Its Learning Algorithm
Issei IDO, Kenji NAKAYAMA, Akihiro HIRANO,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) It is important to realize small size neural networks by which desired functions are achieved. Regarding the activation functions, a method was proposed, in which an abtivation function is composed of several basic functions. The unit input is given as a linear combination of the inputs. The unit output takes the same value for this linear combination. In this paper, a nonlinear function is used to express the unit input in order to expand a degree of freedom. The nonlinear function parameters and the above activation functions are simultaneously trained. More complex classification problems can be solved using a small size neural network. Fast and stable convergence and small size neural networks are confirmed through computer simulation using several classification problems.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Neural networks / Activation function / Nonlinear function / Simultaneous learning / Classification
Paper # NC99-8
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Committee NC
Conference Date 1999/5/20(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Neural Network with Trainable Nonlinear Connections and Activation Functions and Its Learning Algorithm
Sub Title (in English)
Keyword(1) Neural networks
Keyword(2) Activation function
Keyword(3) Nonlinear function
Keyword(4) Simultaneous learning
Keyword(5) Classification
1st Author's Name Issei IDO
1st Author's Affiliation Div. of Electronics and Computer Science, Master's Level Section Graduate School of Natural Science and Technology, Kanazawa University()
2nd Author's Name Kenji NAKAYAMA
2nd Author's Affiliation Div. of Electronics and Computer Science, Master's Level Section Graduate School of Natural Science and Technology, Kanazawa University
3rd Author's Name Akihiro HIRANO
3rd Author's Affiliation Div. of Electronics and Computer Science, Master's Level Section Graduate School of Natural Science and Technology, Kanazawa University
Date 1999/5/20
Paper # NC99-8
Volume (vol) vol.99
Number (no) 58
Page pp.pp.-
#Pages 8
Date of Issue