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Hiroyuki Narihisa

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2008
21 Hirotaka Inoue, Hiroyuki Narihisa: Efficient Incremental Learning with Self-Organizing Neural Grove. DMIN 2008: 578-582
2007
20EEKengo Katayama, Masashi Sadamatsu, Hiroyuki Narihisa: Iterated k-Opt Local Search for the Maximum Clique Problem. EvoCOP 2007: 84-95
19EEHirotaka Inoue, Hiroyuki Narihisa: Efficient Incremental Learning Using Self-Organizing Neural Grove. ICONIP (1) 2007: 762-770
18EEKengo Katayama, Hiroshi Yamashita, Hiroyuki Narihisa: Variable depth search and iterated local search for the node placement problem in multihop WDM lightwave networks. IEEE Congress on Evolutionary Computation 2007: 3508-3515
2005
17EEHiroyuki Narihisa, Takahiro Taniguchi, Michiaki Thuda, Kengo Katayama: Efficiency of Parallel Exponential Evolutionary Programming. ICPP Workshops 2005: 588-595
16EEHirotaka Inoue, Hiroyuki Narihisa: Self-organizing neural grove: effective multiple classifier system with pruned self-generating neural trees. ISCAS (3) 2005: 2502-2505
15EEKengo Katayama, Takahiro Koshiishi, Hiroyuki Narihisa: Reinforcement learning agents with primary knowledge designed by analytic hierarchy process. SAC 2005: 14-21
14EEKengo Katayama, Akihiro Hamamoto, Hiroyuki Narihisa: An effective local search for the maximum clique problem. Inf. Process. Lett. 95(5): 503-511 (2005)
13EEHirotaka Inoue, Hiroyuki Narihisa: Parallel performance of ensemble self-generating neural networks for chaotic time series prediction problems. Systems and Computers in Japan 36(10): 82-92 (2005)
2004
12EEHirotaka Inoue, Hiroyuki Narihisa: Self-organizing Neural Grove: Efficient Multiple Classifier System Using Pruned Self-generating Neural Trees. PPSN 2004: 1113-1122
11EEKengo Katayama, Akihiro Hamamoto, Hiroyuki Narihisa: Solving the maximum clique problem by k-opt local search. SAC 2004: 1021-1025
2003
10EEHirotaka Inoue, Hiroyuki Narihisa: Effective Pruning Method for a Multiple Classifier System Based on Self-Generating Neural Networks. ICANN 2003: 11-18
9EEHirotaka Inoue, Hiroyuki Narihisa: Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks. Multiple Classifier Systems 2003: 256-265
2002
8EEHirotaka Inoue, Hiroyuki Narihisa: Optimizing a Multiple Classifier System. PRICAI 2002: 285-294
2001
7EEHirotaka Inoue, Yoshinobu Fukunaga, Hiroyuki Narihisa: Efficient Hybrid Neural Network for Chaotic Time Series Prediction. ICANN 2001: 712-718
6 Hirotaka Inoue, Hiroyuki Narihisa: Parallel and Distributed Mining with Ensemble Self-Generating Neural Networks. ICPADS 2001: 423-428
2000
5 Kengo Katayama, Masafumi Tani, Hiroyuki Narihisa: Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm. GECCO 2000: 643-650
4EEHirotaka Inoue, Hiroyuki Narihisa: Predicting Chaotic Time Series by Ensemble Self-Generating Neural Networks. IJCNN (2) 2000: 231-236
3 Hirotaka Inoue, Hiroyuki Narihisa: Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging. PAKDD 2000: 177-180
1999
2EEKengo Katayama, Hiroyuki Narihisa: A New Iterated Local Search Algorithm Using Genetic Crossover for the Traveling Salesman Problem. SAC 1999: 302-306
1EEKengo Katayama, Hisayuki Hirabayashi, Hiroyuki Narihisa: Performance analysis for crossover operators of genetic algorithm. Systems and Computers in Japan 30(2): 20-30 (1999)

Coauthor Index

1Yoshinobu Fukunaga [7]
2Akihiro Hamamoto [11] [14]
3Hisayuki Hirabayashi [1]
4Hirotaka Inoue [3] [4] [6] [7] [8] [9] [10] [12] [13] [16] [19] [21]
5Kengo Katayama [1] [2] [5] [11] [14] [15] [17] [18] [20]
6Takahiro Koshiishi [15]
7Masashi Sadamatsu [20]
8Masafumi Tani [5]
9Takahiro Taniguchi [17]
10Michiaki Thuda [17]
11Hiroshi Yamashita [18]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)