Last modefied: June 22, 2020
17.
Kuremoto, T., Hirata,
T., Obayashi, M., Kobayashi,
K., Mabu, S., Search
Heuristics for the Optimization of DBN for Time Series
Forecasting, In Deep Neural Evolution,
Natural Computing Series (eds. H. Iba & N. Noman), Chapter
5, pp. 131-152,
DOI 10.1007/978-981-15-3685-4, Hardcover
ISBN 978-981-15-3684-7, eBook ISBN 978-981-15-3685-4, Springer Nature, May 21,
2020
Masanao
Obayashi, Takkuya Geshi, Takashi
Kuremoto, Shingo Mabu: An Optimization of
Spatio-Spectral Filter Bank Design for EEG Classification, Journal
of Robotics,
Networking and
Artificial Life, Vol. 4, No.2, pp.217-220, March, 2016
.
Shingo Mabu, Masanao Obayashi, Takashi
Kuremoto: An
Evolutionary Algorithm for Making Decision Graphs for Classification
Problems, Journal of
Robotics, Networking
and Artificial Life, Vol. 3, No.1, pp.45-49,
June, 2016
.
Shingo Mabu, Shun Gotoh, Masanao
Obayashi, Takashi
Kuremoto: A
random-forests-based classifier
using class association rules and its application to an intrusion
detection
system,
Artificial Life and Robotics,
Springer, Vol.21, pp.371-377, June, 2016
Obayashi M., Aridome T., Kuremoto T., Mabu S.: Leader-Follower Formation Control Using Cerebellar Perceptron Improved Model with Auto-Structuring, In Proc. 2016 IEEE International Conference on Computational Science and Engineering (2016 CSE), pp. 423-431, Aug. 24-26, 2016 (Paris, France)
Kuremoto T., Kuzukami Y., Obayashi M., Kobayashi K., Mabu S.: RP-AG-SOM: A Growing Self-Organizing Map with Assymetric Neighborhood Function and Variable Radius, In Proc. SAI Intelligent Systems Conference 2016 (IntelliSys 2016), pp. 245-252, Sep. 21-22, 2016 (London, U.K.)
Hirata T., Kuremoto T., Obayashi M., Mabu S., Kobayashi K.: Deep Belief Network using Reinforcement Learning and its Applications to Time Series Forecasting, In Proc. Intern. Conf. Neural Inform. Processing, (ICONIP 2016), Lecture Notes in Computer Science (LNCS), Springer, Vol. 9949, pp. 30-37, Oct. 18-21, 2016 (Kyoto, Japan)
Azakami K., Mabu S., Obayashi M., Kuremoto T.: A Rule-Based Classification System Enhanced by Multi-Objective Genetic Algorithm, Proceedings of The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), pp.650-653, Jan. 19-22, 2017 (Miyazaki, Japan)
Hirata T., Kuremoto T., Obayashi M., Mabu S.: Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning, Proceedings of The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), pp.658-661, Jan. 19-22, 2017 (Miyazaki, Japan)
Kuremoto T., Tokuda S., Obayashi M., Mabu S., Kobayashi K.: An experimental comparison of deep belief nets with different learning methods, Proceedings of 2017 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2017), pp. , 637-640, Feb. 28-Mar. 3, 2017 (Guam, U.S.A.)
Obayashi M., Aridome T., Kuremoto T., Mabu S.: Leader-Follower Adaptive Formation Control without Observation of Agent$B!G(Bs Velocity Using Wavelet Neural Network, Proceedings of 2017 9th International Conference on Machine Learning and Computing (ICMLC 2017), C083, pp.1-8, Feb. 24-26, 2017 (Singapor)
Kuremoto T., Matsusaka H., Obayashi M., Mabu S., Kobayashi K.: A Reinforcement Learning System with Multi-Layered Fuzzy Neural Network, Proceedings of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017 (ICISIP 2017), pp. 444-449, Sep. 7-12 (Hawaii, U.S.A.)
Kuremoto T., Tsuruda T., Obayashi M., Mabu S.: A Sentence Summarizer using Recurrent Neural Network and Attention-Based Encoder, Proceedings of 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017), pp.245-248, Nov. 26-27, 2017 (Shanghai, China)
Fujita K., Mabu S., , Kuremoto T.: Anomaly Dectection of Disaster Areas from Satellite Images Using Convolutional Autoencoder and One-class SVM, Proceedings of The 2018 International Conference on Artificial Life and Robotics (ICAROB 2018), pp.116-119, Feb. 1-4, 2018 (Beppu, Japan)
Shingo Mabu, Shoji Kido, Noriaki Hashimoto, Yasushi Hirano, Takashi Kuremoto: Opacity annotation of diffuse lung deseases using deep convolutional neural network with multi-channel information, Proc. SPIE Medical Image 2018, Houston, U.S.A., Mar. 1, 2018
Mabu S., Kobayashi K., Obayashi M., Kuremoto T.: Unsupervised Image Classification Using Multi-Autoencoder and K-means++, Proceedings of The 2018 International Conference on Artificial Life and Robotics (ICAROB 2018), pp.112-115, Feb. 1-4, 2018 (Beppu, Japa)
Yanping Liu, Changhui Yang, Huang Ling, Shingo Mabu, Takashi Kuremoto: A Visual System of Citrus Picking Robot using Convolutional Neural Networks, The 2018 5th International Conference on System and Informatics (ICSAI2018), pp. 329-334, Nov. 10-12, 2018
Ami Atsumo, Shingo Mabu, Shoji Kido, Yasushi Hirano, Takashi Kuremoto$B!'(BAnalysis of the effects of transfer learning on opacity classification of diffuse lung diseases using convolutional neural network$B!$(BProc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, Singapore, Jan. 6-9, 2019 (The BEST PAPER AWARD)
Shingo Mabu, Shoji Kido, Yasushi Hirano, Takashi Kuremoto$B!'(BUnsupervised and Semi-Supervised Learning for Efficient Opacity Annotation of Diffuse Lung Diseases$B!$(B Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, Singapore, Jan. 6-9, 2019
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16. T. Kuremoto, K. Koga, K. Kobayashi, M. Obayashi: $B!I(BAn Optical Flow Technique's Performance$B!I(B, Proceedings of the 2004 IEICE General Conference, 2004
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HIRANO, Shoji KIDO: $B!H(BUnsupervised
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and
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