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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
NC, MBE |
2019-12-06 17:20 |
Aichi |
Toyohashi Tech |
Regularization Term of WRH Type Used with Moore-Penrose Inverse for Optimizing Neural Networks Yoshifusa Ito (FHU), Hiroyuki Izumi (AGU), Cidambi Srinivasan (UK) MBE2019-60 NC2019-51 |
Weigend et al. proposed an algorithm for optimizing neural networks, which suppressed the notorious over-tting. They at... [more] |
MBE2019-60 NC2019-51 pp.89-94 |
NC, MBE (Joint) |
2018-12-15 14:25 |
Aichi |
Nagoya Institute of Technology |
Approximation of Bayesian Discriminant Functions by Neural Networks with Weight Elimination Yoshifusa Ito (FHU), Hiroyuki Izumi (AGU), Cidambi Srinivasan (UK) NC2018-34 |
[more] |
NC2018-34 pp.35-40 |
MBE, NC (Joint) |
2017-12-16 10:30 |
Aichi |
Nagoya University |
Application of ELM for Approximating Bayesian Discriminant Functions Yoshifusa Ito (FHU), Hiroyuki Izumi (AGU), Cidambi Srinivasan (UK) NC2017-40 |
[more] |
NC2017-40 pp.1-6 |
NC, MBE (Joint) |
2012-12-12 17:30 |
Aichi |
Toyohashi University of Technology |
Learning of Mahalanobis discriminant functions by a neural network
-- For non-normally distributed signals -- Hiroyuki Izumi (AGU), Yoshifusa Ito (AMU), Cidambi Srinivasan (UKY) NC2012-90 |
[more] |
NC2012-90 pp.79-84 |
NC, MBE (Joint) |
2008-03-12 16:50 |
Tokyo |
Tamagawa Univ |
Conversion of inner parameters to outer parameters Yoshifusa Ito, Hiroyuki Izumi (Aichi-Gakuin Univ.) NC2007-127 |
To overcome difficultites in learning of $a$ three-layer nural network, construction of $a$ network with hidden-layer un... [more] |
NC2007-127 pp.91-96 |
NC |
2007-06-15 13:25 |
Okinawa |
OIST Seaside House |
Learning of Neural Networks with Dichotomic Random Teacher Signals Yoshifusa Ito (AGU), Cidambi Srinivasan (UKY), Hiroyuki Izumi (AGU) NC2007-21 |
Learning with dichotomic random teacher signals is a hard task for neural networks, because the learning cannot be compl... [more] |
NC2007-21 pp.75-80 |
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