{"id":61,"date":"2024-09-19T06:50:15","date_gmt":"2024-09-19T06:50:15","guid":{"rendered":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp\/?page_id=61"},"modified":"2024-10-15T13:14:40","modified_gmt":"2024-10-15T04:14:40","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/publications\/","title":{"rendered":"\u696d\u7e3e\u30ea\u30b9\u30c8"},"content":{"rendered":"<img decoding=\"async\" src=\"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp\/wp-content\/uploads\/2024\/09\/publications_main.jpg\" alt=\"\" \/>\r\n\r\n<section id=\"jornal\">\r\n<h2>Jornal Paper<\/h2>\r\n\r\n<ul class=\"c-list-publications\">\r\n<li>Visual analytics of set data for knowledge discovery and member selection support<br>Ryuji Watanabe, Hideaki Ishibashi, Tetsuo Furukawa<br>Decision Support Systems, Vol.152, January 2022<\/li>\r\n<li>\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u306b\u3088\u308b\u30de\u30eb\u30c1\u30d3\u30e5\u30fc\u95a2\u4fc2\u30c7\u30fc\u30bf\u306e\u5305\u62ec\u7684\u53ef\u8996\u5316 <font color=\"red\">\u203b\u53d7\u8cde<\/font><br>\u7c73\u7530\u572d\u4f51, \u4e2d\u91ce\u8cb4\u7406\u535a, \u5800\u5c3e\u6075\u4e00, \u53e4\u5ddd\u5fb9\u751f<br>\u77e5\u80fd\u3068\u60c5\u5831, Vol.30, No.2, pp.525-536, 2018<li>\r\n<li>Hierarchical Tensor SOM Network for Multilevel\u2013Multigroup Analysis<br>Hideaki Ishibashi, Tetsuo Furukawa<br>Proceeding of Neural Processing Letters, 47, 3, pp.1011-1025, 2018<\/li>\r\n<li>Hierarchical Tensor Manifold Modeling for Multi-Group Analysis<br>Hideaki Isibashi, Masayoshi ERA and Tetsuo FURUKAWA<br>IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E101-A, No.11, pp.1745-1755, 2018<\/li>\r\n<li>Distance Metric Learning for the Self-Organizing Map Using a Co-Training Approach<br>Keisuke Yoneda, Tetsuo Furukawa<br>International Journal of Innovative Computing, Information and Control, Vol.14, Number6, pp.2343-2351, 2018 <\/li>\t\t\t\t\t\r\n<li>Hierarchical Manifold Modeling for Multi-Team Analysis<br>Hideaki Ishibashi, Masayoshi Era and Tetsuo Furukawa<br>Neural Processing Letters, Vol.E101.A, Issue 11, pp.1745-1755, 2018<\/li>\r\n<li>Hierarchical Tensor SOM Network for Multilevel\u2013Multigroup Analysis<br>Hideaki Ishibashi, Tetsuo Furukawa<br>Neural Processing Letters, Vol.47, Issue 3, pp.1011\u20131025, 2018<\/li>\r\n<li>\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u306b\u3088\u308b\u30de\u30eb\u30c1\u30d3\u30e5\u30fc\u95a2\u4fc2\u30c7\u30fc\u30bf\u306e\u5305\u62ec\u7684\u53ef\u8996\u5316<br>\u7c73\u7530\u572d\u4f51, \u4e2d\u91ce\u8cb4\u7406\u535a, \u5800\u5c3e\u6075\u4e00, \u53e4\u5ddd\u5fb9\u751f<br>\u77e5\u80fd\u3068\u60c5\u5831, Vol.30, No.2, pp.501-512, 2018<\/li>\r\n<li>Hierarchical Tensor SOM Network for Multilevel\u2013Multigroup Analysis<br>Hideaki Ishibashi, Tetsuo Furukawa<br>Neural Processing Letters, pp.1-15, 2017<\/li>\r\n<li>Tensor SOM\u306b\u3088\u308b\u611f\u6027\u306e\u8a55\u4fa1\u8005\u30fb\u8a55\u4fa1\u5bfe\u8c61\u30fb\u8a55\u4fa1\u8a9e\u306e\u540c\u6642\u5206\u6790<br>\u7cf8\u6c38 \u606d\u5e73, \u5ca9\u5d0e \u4e98, \u4e0a\u6751 \u6d0b\u5e73, \u5409\u7530 \u9999, \u53e4\u5ddd \u5fb9\u751f<br>\u77e5\u80fd\u3068\u60c5\u5831, Vol.29, No.6, pp.661-669, 2017<\/li>\r\n<li>tensor SOM and tensor GTM: nonlinear tensor analysis by topographic mappings<br>Iwasaki, T., Furukawa, T.<br>Neural Networks, Vol.77, pp.107-125, 2016<\/li>\r\n<li>\u81ea\u5df1\u6210\u9577\u578b\u30e2\u30b8\u30e5\u30e9\u30fc\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u81ea\u5f8b\u79fb\u52d5\u30ed\u30dc\u30c3\u30c8\u306b\u304a\u3051\u308b\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u5730\u56f3\u306e\u30aa\u30f3\u30e9\u30a4\u30f3\u69cb\u7bc9<br>\u5ddd\u7551 \u5ba3\u4e4b\uff0c\u5fb3\u6c38 \u61b2\u6d0b\uff0c\u53e4\u5ddd \u5fb9\u751f<br>ISSN, Vol.25-2, pp. 659-675, 2013<\/li>\r\n<li>\u9ad8\u968eSOM\u306b\u3088\u308b\u5f62\u72b6\u8868\u73fe\u30de\u30c3\u30d7\u306e\u81ea\u5df1\u7d44\u7e54\u5316-\u30c8\u30dd\u30ed\u30b8\u30fc\u62d8\u675f\u306e\u306a\u3044\u5f62\u72b6\u7a7a\u9593\u6cd5-<br>\u5927\u8c37 \u8aa0, \u53e4\u5ddd \u5fb9\u751f<br>ISSN, Vol.25-2, pp.701-720, 2013<\/li>\r\n<li>shape space estimation by higher-rank of SOM<br>Yakushiji, S., Furukawa, T.<br>Neural Computing and Applications, Vol.22, pp.1267-1277, 2012<\/li>\r\n<li>self evolving modular network<br>Tokunaga, K., Kawabata, N., Furukawa, T.<br>IEICE, Vol.E95-D, No.5, pp.1506-1518, 2012<\/li>\r\n<li>\u9ad8\u968e\u5316SOM\u306b\u3088\u308b\u5f62\u72b6\u8868\u73fe\u30de\u30c3\u30d7<br>\u85ac\u5e2b\u5bfa \u7fd4\uff0c \u53e4\u5ddd \u5fb9\u751f<br>ISSN, Vol.24, No.2, pp.648-659, 2012<\/li>\r\n<li>the self-organizing adaptive controller<br>Minatohara, T., Furukawa, T.<br>IJICIC, Vol.7, No.4, 2011<\/li>\r\n<li>some learning properties of modular network SOMs<br>Takeda, M., Ikeda, K., Furukawa, T.<br>SICE JCMSI, Vol.3, No.1, pp.15-19.\/li>\r\n<li>building a cognitive map using an SOM2<br>Tokunaga, K., Furukawa, T.<br>JAMRIS, Vol.4, No.2, pp.39-47.<\/li>\r\n<li>modular network SOM<br>Tokunaga K., Furukawa, T.<br>Neural Networks, Vol.22, pp.82-90, 2009.<\/li>\r\n<li>SOM of SOMs<br>Furukawa, T.<br>Neural Networks, Vol.22, Issue 4, pp.463-478, 2009.<\/li>\r\n<li>task segmentation in a mobile robot by mnSOM and clustering with spatio-temporal contiguity<br>Aziz Muslim, M., Ishikawa, M., Furukawa, T.<br>IJICIC, Vol.5, No.4, pp.865-875, 2009<\/li>\r\n<li>RBFxSOM: an efficient algorithm for large-scale multi-system learning<br>Ohkubo, T., Tokunaga, K., Furukawa, T.<br>IEICE Trans.Inf.&#038; Syst, Vol.E92-D, No.7, pp.1388-1396<\/li>\r\n<li>\u9ad8\u968e\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u306e\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u5316: \u77e5\u8b58\u8868\u73fe\u3092\u81ea\u5df1\u7d44\u7e54\u5316\u3059\u308b\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u306e\u958b\u767a<br>\u91d1\u5b50 \u5b97\u53f8\uff0c\u7530\u5411 \u6a29\uff0c\u5fb3\u6c38 \u61b2\u6d0b\uff0c\u53e4\u5ddd \u5fb9\u751f<br>ISSN, Vol.21, No.5, pp.870-883, 2009<\/li>\r\n<li>a class density approximation neural network for improving the generalization of Fisherface<br>Jiang, J., Zhang, L., Furukawa, T.<br>Neurocomputing Vol.71, Issues 16-18, pp.3239-3246, 2008<\/li>\r\n<li>\u9069\u5fdc\u6027\u3068\u6c4e\u5316\u6027\u3092\u8003\u616e\u3057\u305f\u81ea\u5df1\u7d44\u7e54\u5316\u9069\u5fdc\u5236\u5fa1\u5668<br>\u6e4a\u539f \u54f2\u4e5f\uff0c\u53e4\u5ddd \u5fb9\u751f<br>IEICED, Vol.J91-D, No.4, pp.1142-1149, 2008<\/li>\r\n<li>task segmentation in a mobile robot by mnSOM: a new approach to training expert modules<br>Aziz Muslim, M., Ishikawa, M., Furukawa, T.<br>Neural Computing &#038; Applications, 2007<\/li>\r\n<li>\u6c34\u4e2d\u30ed\u30dc\u30c3\u30c8\u306b\u304a\u3051\u308b\u81ea\u5df1\u7d44\u7e54\u7684\u884c\u52d5\u7372\u5f97\u30b7\u30b9\u30c6\u30e0 -\u7b2c\u4e00\u5831:\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u3092\u7528\u3044\u305f\u904b\u52d5\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u306e\u63d0\u6848-<br>\u897f\u7530 \u5468\u5e73, \u77f3\u4e95 \u548c\u7537, \u53e4\u5ddd \u5fb9\u751f<br>JASNAOE Vol.3, pp.205-213, 2006<\/li>\r\n<li>modular network SOM : self-organizing Maps in function space<br>Tokunaga, K., Furukawa, T., Yasui, S.<br>NIPLR, Vol.9, No.1, pp.15-22, 2005<\/li>\r\n<li>\u95a2\u6570\u7a7a\u9593\u578bSOM<br>\u5fb3\u6c38 \u61b2\u6d0b, \u809d\u4ed8 \u8b19\u4e8c, \u5b89\u4e95 \u6e58\u4e09, \u53e4\u5ddd \u5fb9\u751f<br>JNNS, Vol.12, No.1, pp.39-51, 2005<\/li>\r\n<li>asymmetric temporal properties in the receptive field of retinal transient amacrine cells<br>Djupsund, K., Furukawa, T., Yasui, S., Yamada, M.<br>J.Gen.Physiol., Vol.122, 2003<\/li>\r\n<li>nitric oxide controls the light adaptive chromatic difference in receptive field size of H1 horizontal cell network in carp retina<br>Furukawa, T., Petruv, R., Yasui, S., Yamada, M., Djamgoz, M.B.<br>Experimental Brain Reserch, 147, 3, pp.296-304, 2002<\/li>\r\n<li>\u30c7\u30fc\u30bf\u306e\u30af\u30e9\u30b9\u632f\u308a\u5206\u3051\u3068\u30af\u30e9\u30b9\u5225\u30e2\u30c7\u30eb\u306e\u540c\u6642\u63a8\u5b9a\u6cd5<br>\u53e4\u5ddd \u5fb9\u751f<br>JNNS, 9, 2, pp.92-102, 2002<\/li>\r\n<li>effects of nitric oxide, light adaptation and APB on spectral characteristics of H1 horizontal cells in carp retina<br>Yamada, M., Fraser, SP., Furukawa, T., Hirasawa, H., Katano, K., Djamgoz, M., Yasui, S.<br>Neuroscience Research, Vol.35, Issue 4, pp.309-319, 1999<\/li>\r\n<li>nitric oxide, 2-amino-4-phosphonobutyric acid and light\/dark adaptation modulate short-wavelength-sensitive synaptic transmission to retinal<br>Furukawa, T., Yamada, M., Petruv, R., Djamgoz, MB., Yasui, S.<br>Neuroscience Research, Vol.27, Issue 1, pp.65-74, 1997<\/li>\r\n<li>plasticity of center-surround opponent receptive fields in real and artificial neural systems of vision<br>Yasui, S., Furukawa, T., Yamada, M., Saito, T.<br>NIPS, Vol.8, pp.159-165, 1995<\/li>\r\n<\/ul>\r\n<p class=\"pagetop\"><a href=\"#\">\u25b3\u30da\u30fc\u30b8\u30c8\u30c3\u30d7\u3078\u623b\u308b<\/a><\/p>\r\n<\/section>\r\n\r\n<section id=\"conference\">\r\n<h2>International Conference<\/h2>\t\t\t\t\t\r\n\r\n<ul class=\"c-list-publications\">\r\n<li>Simultaneous Meta- modeling of Dynamics and Kinematics based on the Hierarchical Manifold Modeling <font color=\"red\">\u203b\u53d7\u8cde <\/font><br>Daiki Tanka, Hideaki Ishibashi and Tetsuo Furukawa<br>\u7b2c3\u56de\u30cb\u30e5\u30fc\u30ed\u30e2\u30eb\u30d5\u30a3\u30c3\u30afAI\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u56fd\u969b\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0, 2021<\/li>\r\n<li>Sparse approximation of unsupervised kernel regressionfor large scale relational data<br>Kazuki Miyazaki and Hideaki Ishibashi and Tetsuo Furukawa<br>\u7b2c3\u56de\u30cb\u30e5\u30fc\u30ed\u30e2\u30eb\u30d5\u30a3\u30c3\u30afAI\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u56fd\u969b\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0, 2021<\/li>\r\n<li>Meta-modeling of manifold models for dynamical systems through biased optimal transport distance minimization<br>Seitaro Nakashima, Hideaki Ishibashi, andTetsuoFurukawa<br>\u7b2c3\u56de\u30cb\u30e5\u30fc\u30ed\u30e2\u30eb\u30d5\u30a3\u30c3\u30afAI\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u56fd\u969b\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0, 2021<\/li>\r\n<li>Scalable manifold modeling by Nadaraya-Watson kernel regression <font color=\"red\">\u203b\u53d7\u8cde <\/font><br>Kazuki Miyazaki, Shuhei Takano, Ryo Tsuno, Hideaki Ishibashi, Tetsuo Furukawa<br>ICICIC2021, 2021<\/li>\r\n<li>Fashion outfit retrieval via hashtag search and visually-assisted browsing on jointed manifold models<br>Shuri Hirowatari, Takuro Ishida, Tohru Iwasaki and Tetsuo Furukawa<br>ICICIC2021, 2021<\/li>\r\n<li>Simultaneous Visualization of Documents, Words and Topics by Tensor Self-Organizing Map and Non-negative Matrix Factorization<br>Kazuki Noguchi, Takuro Ishida, Tetsuo Furukawa<br>SCIS-ISIS2020, 2020<\/li>\r\n<li>Space-and-Cost-Efficient Neural Control\/Sensory Element Using an Analog FPGAb<br>Fuminori Kobayashi, Tetsuo Furukawa<br>ICSSE2019, 2019<\/li>\r\n<li>Simultaneous Analysis of Subjective and Objective Data Using Coupled Tensor Self-organizing Maps: Wine Aroma Analysis with Sensory and Chemical Data<br>Keisuke Yoneda, Kimihiro Nakano, Keiichi Horio, Tetsuo Furukawa<br>ICONIP2018, 2018<\/li>\r\n<li>Distance Metric Learning for the Self-Organizing Map Using a Co-Training Approach<br>Keisuke Yoneda, Tetsuo Furukawa<br>ICICIC2018, 2018<\/li>\r\n<li>ultilevel-Multigroup Analysis Discovering Member Correspondence between Groups<br>Hideaki Ishibashi, Masayoshi Era, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa<br>SISA2017, 2017<\/li>\r\n<li>self-organizing maps for multi system and multi view datasets<br>Ishibashi, H., Furukawa, .<br>SCIS-ISIS2016, 2016<\/li>\r\n<li>rating-scale questionnaire survey analysis using SOM-based nonlinear tensor decomposition<br>Ishibashi, H., Iwasaki, T., Date, Y., Furukawa, T.<br>SCIS-ISIS2016, 2016<\/li>\r\n<li>multilevel-multigroup analysis using a hierarchical tensor SOM network<br>Ishibashi, H., Shinriki, R., Isogai, H., Furukawa, T.<br>ICONIP2016, Neural Information Processing, Vol.9949, pp 459-466, 2016<\/li>\r\n<li>research on multi-system learning theory: a case study of brain-inspired system<br>Furukawa, T., Natsume, K., Ohkubo, T.<br>SCIS-ISIS2012, pp.311-314, 2012<\/li>\r\n<li>shape space estimation by SOM2<br>Yakushiji, S., Furukawa, T.<br>ICONIP2011, LNCS, Vol.7063, pp.618-627, 2011<\/li>\r\n<li>requirements for the learning of multiple dynamics<br>Ohkubo, T., Furukawa, T., Tokunaga, K.<br>WSOM2011, LNCS, Vol.6731, pp.101-110, 2011<\/li>\r\n<li>multi-dynamics learning algorithm based on SOM2<br>Matsushita, S., Ohkubo, T., Furukawa, T.<br>ICCN2011, pp.180, 2011<\/li>\r\n<li>an adaptive controller system using mnSOM (2nd report: implementation into an autonomous underwater robot)<br>Takemura, Y., Ishitsuka, M., Nishida, S., Ishii, K., Furukawa, T.<br>BraiIT2010, Studies in Computational Intelligence, Vol.266, pp.91-96, 2010<\/li>\r\n<li>line image classification by NGxSOM : application to handwritten character recognition<br>Otani, M., Gunya, K., Furukawa, T.<br>WSOM2009, LNCS Vol.5629, pp.219-227, 2009<\/li>\r\n<li>an online adaptation control system using mnSOM <br>Nishida, S., Ishii, K., Furukawa, T.<br>ICONIP2006, LNCS, Vol.4232, pp.935-942, 2006<\/li>\r\n<li>generalization of the self-organizing map: from artificial neural networks to artificial cortexes,<br>Furukawa, T., Tokunaga, K.<br>ICONIP2006, LNCS, Vol.4232, pp.943-949, 2006<\/li>\r\n<li>SOM of SOMs : an extension of SOM from \u2018Map\u2019 to \u2018Homotopy\u2019,<br>Furukawa, T. <br>ICONIP2006, LNCS, Vol.4232, pp.950-957, 2006<\/li>\r\n<li>modular network SOM: theory, algorithm and applications,<br>Tokunaga, K., Furukawa, T.<br>ICONIP2006, LNCS, Vol.4232, pp.958-967, 2006<\/li>\r\n<li>improving the generalization of fisherface by training class selection using SOM2,<br>Jiang, J., Zhang, L., Furukawa, T.<br>ICONIP2006, LNCS, Vol.4233, pp.278-285, 2006<\/li>\r\n<li>SOM of SOMs : self-organizing map which maps a group of self-organizing maps<br>Furukawa, T.<br>LNCS2005, Vol.3696, pp.391-396, 2005<\/li>\r\n<li>modular network SOM(mnSOM): from vector space to function space<br>Furukawa, T., Tokunaga, K., Moroshita, K., Yasui, S.<br>IJCNN2005, pp.1581-1586, Canada, 2005.8<\/li>\r\n<\/ul>\r\n<p class=\"pagetop\"><a href=\"#\">\u25b3\u30da\u30fc\u30b8\u30c8\u30c3\u30d7\u3078\u623b\u308b<\/a><\/p>\r\n<\/section>\r\n\r\n<section id=\"books\">\r\n<h2>Boooks<\/h2>\r\n<ul class=\"c-list-publications\">\r\n<li>\u300c\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u3068\u305d\u306e\u5fdc\u7528\u300d\uff08\u7de8\u30fb\u5fb3\u9ad8\u5e73\u8535, \u5927\u5317\u6b63\u662d, \u85e4\u6751\u559c\u4e45\u90ce\uff09<br>\u7b2c\uff16\u7ae0 mnSOM: \u30d1\u30fc\u30c4\u4ea4\u63db\u53ef\u80fd\u306a\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u578bSOM<br>\u53e4\u5ddd\u5fb9\u751f<br>p.69-84, \u30b7\u30e5\u30d7\u30ea\u30f3\u30ac\u30fc\u30fb\u30b8\u30e3\u30d1\u30f3, 2007<\/li>\r\n<li>\u300c\u611f\u899a\u60c5\u5831\u51e6\u7406\u300d\uff08\u7de8\u30fb\u5b89\u4e95\u6e58\u4e09)<br>\u7b2c\uff12\u7ae0\u3000\u8996\u899a<br>\u53e4\u5ddd\u5fb9\u751f, \u516b\u6728\u54f2\u4e5f<br>pp.13-62, \u30b3\u30ed\u30ca\u793e, 2004.3<\/li>\r\n<\/ul>\r\n<p class=\"pagetop\"><a href=\"#header\">\u25b3\u30da\u30fc\u30b8\u30c8\u30c3\u30d7\u3078\u623b\u308b<\/a><\/p>\r\n<\/section>\r\n\r\n<section id=\"others\">\r\n<h2>Others<\/h2>\r\n<ul class=\"c-list-publications\">\r\n<li>Unsupervised Kernel Regression with Landmarks for Large Relational Data \uff5e Toward Visual Analytics Method for Complex Relational Data \uff5e<br>\u9ad8\u91ce\u4fee\u5e73\u30fb\u6d25\u91ce\u3000\u9f8d\u30fb\u91ce\u53e3\u79d1\u745e\u7a00\u30fb\u5bae\u5d0e\u4e00\u5e0c\u30fb\u53e4\u5ddd\u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 2020<\/li>\r\n<li>\u30de\u30eb\u30c1\u30bf\u30b9\u30af\u591a\u69d8\u4f53\u30e2\u30c7\u30ea\u30f3\u30b0\u306e\u89e3\u304f\u3079\u304d\u554f\u984c\u306f\u306a\u306b\u304b\uff1f \uff5e \u76f4\u7a4d\u6f5c\u5728\u7a7a\u9593\u3068\u95a2\u6570\u7a7a\u9593\u306e\u30a2\u30d7\u30ed\u30fc\u30c1 \uff5e <font color=\"red\">\u203b\u53d7\u8cde<\/font><br>\u6d25\u91ce\u9f8d\u30fb\u77f3\u6a4b\u82f1\u6717\u30fb\u53e4\u5ddd\u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 2020<\/li>\r\n<li>\u6587\u66f8\u30fb\u5358\u8a9e\u306e\u540c\u6642\u5206\u5e03\u30e2\u30c7\u30eb\u5316\u306b\u3088\u308b\u4e21\u8005\u306e\u95a2\u4fc2\u6027\u53ef\u8996\u5316<br>\u77f3\u7530\u7422\u6717, \u6ce2\u7530\u91ce\u5275, \u53e4\u5ddd\u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 2019<\/li>\r\n<li>\u95a2\u4fc2\u30c7\u30fc\u30bf\u306e\u76f4\u7a4d\u7a7a\u9593\u3078\u306e\u57cb\u3081\u8fbc\u307f\u306b\u3088\u308b\u53ef\u8996\u5316<br>\u5bae\u5d0e\u4e00\u5e0c, \u6e21\u8fba\u9f8d\u4e8c, \u53e4\u5ddd\u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 2019<\/li>\r\n<li>Multi-Level SOM\u306b\u3088\u308b\u30e1\u30f3\u30d0\u30fc\u69cb\u6210\u306b\u3088\u308b\u30c1\u30fc\u30e0\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306e\u53ef\u8996\u5316<br>\u7028\u91ce\u6d66\u8cab\u592a, \u77f3\u6a4b\u82f1\u6717, \u53e4\u5ddd\u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 2019<\/li>\r\n<li>Optimal Transport based Autoencoder for class and style Disentanglement<br>Florian Tambon, Tetsuo Furukawa<br>NC, \u4fe1\u5b66\u6280\u5831, 2019<\/li>\r\n<li>Tensor SOM\u3092\u7528\u3044\u305f\u30b0\u30eb\u30fc\u30d7\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u306b\u304a\u3051\u308b\u5e7c\u5150\u9593\u306e\u30a4\u30f3\u30bf\u30e9\u30af\u30b7\u30e7\u30f3\u306e\u53ef\u8996\u5316<br>\u6960\u5143\u5553\u4ecb, \u5800\u5c3e\u6075\u4e00, \u53e4\u5ddd\u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 2019<\/li>\r\n<li>\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u8868\u73fe\u3092\u7528\u3044\u305fTensor SOM\u306e\u9023\u7d9a\u8868\u73fe\u5316<br>\u6e21\u8fba\u9f8d\u4e8c, \u5bae\u5d0e\u4e00\u5e0c, \u53e4\u5ddd\u5fb9\u751f<br>JNNS2019, pp48-49, 2019<\/li>\r\n<li>Tensor SOM\u306b\u3088\u308bKWM\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316 -\u6559\u5e2b\u306b\u3088\u308b\u6388\u696d\u6539\u5584\u3068\u751f\u5f92\u306e\u72b6\u614b\u628a\u63e1\u3092\u76ee\u7684\u3068\u3057\u3066-<br>\u5ca9\u6b66 \u6f84\uff0c \u53e4\u5ddd \u5fb9\u751f<br>\u60c5\u5831\u51e6\u7406\u5b66\u4f1a, \u7814\u7a76\u5831\u544a\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3068\u6559\u80b2\uff08CE), 2018-CE-143\u300012\u53f7\u3000pp.1-7, 2018<\/li>\r\n<li>SOM\u306b\u3088\u308b\u30de\u30eb\u30c1\u30bf\u30b9\u30af\u5b66\u7fd2\u306e\u5b9f\u73fe<br>\u6bd4\u5609 \u4e00\u5fd7\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.117, No.417, NC2017-55, pp.29-33, 2018<\/li>\r\n<li>Tensor SOM\u306e\u968e\u5c64\u5316\u306b\u3088\u308b\u30de\u30eb\u30c1\u30b0\u30eb\u30fc\u30d7\u89e3\u6790<br>\u6c5f\u826f \u660c\u7965\uff0c\u77f3\u6a4b \u82f1\u6717\uff0c\u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.117, No.417, NC2017-54, pp.23-28, 2018<\/li>\r\n<li>\u975e\u8ca0\u30ab\u30fc\u30cd\u30eb\u5e73\u6ed1\u5316\u306b\u3088\u308b\u9023\u7d9a\u6f5c\u5728\u5909\u6570\u30e2\u30c7\u30eb\u306e\u8a66\u307f<br>\u77f3\u6a4b \u82f1\u6717, \u5ca9\u5d0e \u4e98, \u6e21\u8fba \u9f8d\u4e8c, \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.117, No.325, NC2017-32, pp. 29-34, 2018<\/li>\r\n<li>\u4e8b\u5f8c\u5206\u5e03\u63a8\u5b9a\u3055\u308c\u305f\u30ac\u30a6\u30b9\u904e\u7a0b\u9593\u306eKL\u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u306f\u6709\u9650\u6b21\u5143\u306e\u6b63\u898f\u5206\u5e03\u9593\u306eKL\u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u3067\u8a55\u4fa1\u3067\u304d\u308b<br>\u77f3\u6a4b \u82f1\u6717, \u53e4\u5ddd \u5fb9\u751f, \u8d64\u7a42 \u662d\u592a\u90ce<br>IBIS2017, \u4fe1\u5b66\u6280\u5831117, 55-160, 017<\/li>\r\n<li>Continuous latent variable model by non-negative kernel smoother<br>Ishibashi, H., Watanabe, R., Iwasaki, T., Furukawa, T.<br>JNNS2017, P-07, 2017<\/li>\r\n<li>Visualization of multi-relational data by combined Tensor SOM<br>Yoneda, K., Furukawa, T.<br>JNNS2017, P-04, 2017<\/li>\r\n<li>Tensor SOM\u306b\u3088\u308b\u98a8\u666f\u753b\u50cf\u306e\u611f\u6027\u8a55\u4fa1\u89e3\u6790 \u301c\u8a55\u4fa1\u8005\u30fb\u8a55\u4fa1\u5bfe\u8c61\u30fb\u8a55\u4fa1\u8a9e\u306e\u540c\u6642\u5206\u6790\u301c<br>\u7cf8\u6c38 \u606d\u5e73\uff0c \u5ca9\u5d0e \u4e98\uff0c \u5409\u7530 \u9999\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.117, No.109, NC2017-12, pp. 45-50<\/li>\r\n<li>\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u9593\u306e\u30e1\u30c8\u30ea\u30c3\u30af\u5b66\u7fd2\u306b\u3088\u308b\u30de\u30eb\u30c1\u30d3\u30e5\u30fc\u30c7\u30fc\u30bf\u306e\u975e\u7dda\u5f62\u6b63\u6e96\u76f8\u95a2\u5206\u6790<br>\u7c73\u7530 \u572d\u4f51\uff0c \u4e2d\u91ce \u8cb4\u7406\u535a\uff0c \u5800\u5c3e\u6075\u4e00\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.117, No.64, NC2017-1, pp. 1-6<\/li>\r\n<li>\u591a\u8996\u70b9\u30c7\u30fc\u30bf\u306e\u6f5c\u5728\u8996\u70b9\u63a8\u5b9a\u6cd5<br>\u77f3\u6a4b \u82f1\u6717\uff0c \u795e\u529b \u4eae\u592a\uff0c \u78ef\u8c9d \u6d69\u4e45\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, pp.37-41, 2017<\/li>\r\n<li>tensor SOM network\u306b\u3088\u308b\u8907\u5408\u30c6\u30f3\u30bd\u30eb\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u3068\u60c5\u5831\u4f1d\u64ad<br>\u6238\u5cf6 \u60a0\u8cb4\uff0c \u7c73\u7530 \u572d\u4f51\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, pp.83-88, 2017<\/li>\r\n<li>wing \u7d50\u5408\u578bSOM\u306b\u3088\u308b\u30de\u30eb\u30c1\u30e2\u30c0\u30ea\u30c6\u30a3\u89e3\u6790<br>\u7c73\u7530 \u572d\u4f51, \u4e2d\u91ce \u8cb4\u7406\u535a, \u5800\u5c3e \u6075\u4e00<br>FIT2016, \u7b2c15\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\u8b1b\u6f14\u8ad6\u6587\u96c6, G-006, pp.291-296, 2016<\/li>\r\n<li>tensor SOM\u3092\u7528\u3044\u305f\u96fb\u5b50\u30e1\u30fc\u30eb\u306e\u30c8\u30d4\u30c3\u30af\u3068\u4eba\u9593\u95a2\u4fc2\u306e\u540c\u6642\u53ef\u8996\u5316<br>\u6ce2\u7530\u91ce \u5275\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.115, No.426, pp.61-66, January, 2016<\/li>\r\n<li>tensor SOM\u306b\u3088\u308be-mail\u30c7\u30fc\u30bf\u306e\u30c8\u30d4\u30c3\u30af\u30fb\u30ed\u30fc\u30eb\u306e\u540c\u6642\u53ef\u8996\u5316<br>\u6ce2\u7530\u91ce \u5275, \u53e4\u5ddd \u5fb9\u751f<br>IBIS2015, \u306a\u3057, D-86, November, 2015<\/li>\r\n<li>\u30c6\u30f3\u30bd\u30ebSOM\u306b\u3088\u308b\u4f01\u696d\u5185\u30c1\u30fc\u30e0\u5206\u6790<br>\u5ca9\u5d0e \u4e98\uff0c \u4f0a\u9054 \u6d0b\u7950\uff0c \u53e4\u5ddd \u5fb9\u751f<br>FAN2015, B302, September, 2015<\/li>\r\n<li>\u30c7\u30fc\u30bf\u306b\u6f5c\u5728\u3059\u308b\u89b3\u6e2c\u8996\u70b9\u306e\u63a8\u5b9a\u6cd5-\u89b3\u6e2c\u4f9d\u5b58\u30fb\u975e\u4f9d\u5b58\u6210\u5206\u3078\u306e\u5206\u89e3-<br>\u77f3\u6a4b \u82f1\u6717\uff0c \u53e4\u5ddd \u5fb9\u751f<br>JNNS2015, pp.62, 63, September, 2015<\/li>\r\n<li>\u30c6\u30f3\u30bd\u30ebSOM\u306b\u3088\u308b\u81ea\u5bb6\u7528\u8eca\u30a4\u30f3\u30d1\u30cd\u306e\u5370\u8c61\u8a55\u4fa1<br>\u4f0a\u9054 \u6d0b\u7950\uff0c \u5ca9\u5d0e \u4e98\uff0c \u53e4\u5ddd \u5fb9\u751f<br>FSS2015, TC1-3, 2015<\/li>\r\n<li>\u30c6\u30f3\u30bd\u30ebSOM\u306b\u3088\u308b\u95a2\u4fc2\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316<br>\u5ca9\u5d0e \u4e98\uff0c \u53e4\u5ddd \u5fb9\u751f,<br>FSS2015, TC1-2, 2015<\/li>\r\n<li>\u30de\u30eb\u30c1\u6f5c\u5728\u7a7a\u9593GTM\u306b\u3088\u308b\u30c6\u30f3\u30bd\u30eb\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316<br>\u6bd4\u5609 \u4e00\u5fd7\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.115, No.112, pp. 33-38, 2015<\/li>\r\n<li>\u6b20\u640d\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308bTensor SOM\u306e\u30ed\u30d0\u30b9\u30c8\u6027<br>\u8107\u7530 \u9756\u5f18\uff0c \u5ca9\u5d0e\u3000\u4e98\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.114, No.437, NC2014-61, pp. 21-26, 2015<\/li>\r\n<li>\u9ad8\u968eSOM\u306e\u5f62\u5f0f\u30cb\u30e5\u30fc\u30ed\u30f3\u5b9f\u88c5\uff1a\u8208\u596e\u4f1d\u64ad\u5834\u306b\u3088\u308b\u8a18\u61b6\u306e\u8ee2\u9001\u3068\u7d71\u5408<br>\u4f50\u4fdd \u96c4\u592a\uff0c \u590f\u76ee \u5b63\u4ee3\u4e45\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.114, No.437, NC2014-62, pp. 27-32, 2015<\/li>\r\n<li>nonlinear tensor decomposition using generative topographic mapping<br>Higa, K., Iwasaki, T., Furukawa, T. <br>IBIS2014, D-16, 2014<\/li>\r\n<li>tensor SOM \u306b\u3088\u308b movieLens dataset \u306e\u89e3\u6790<br>\u4f0a\u9054 \u6d0b\u7950, \u8107\u7530 \u9756\u5f18, \u5ca9\u5d0e \u4e98, \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.113, No.500, pp63-68, 2014<\/li>\r\n<li>\u30af\u30e9\u30b9\u63a8\u5b9a\u578b\u9ad8\u968eSOM\u306b\u3088\u308b\u30e9\u30a4\u30d5\u30d1\u30bf\u30fc\u30f3\u306e\u53ef\u8996\u5316: \u30e6\u30fc\u30b6\u306e\u74b0\u5883\u3092\u8003\u616e\u3057\u305f\u89e3\u6790\u624b\u6cd5<br>\u77f3\u6a4b \u82f1\u6717, \u5ca9\u5d0e \u4e98, \u5800\u5c3e \u6075\u4e00, \u96e3\u6ce2 \u79c0\u884c, \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.113, No.148, NC2013-20, pp. 29-34, 2013<\/li>\r\n<li>\u5f62\u72b6\u7a7a\u9593\u6cd5\u3092\u7528\u3044\u305f\u706b\u661f\u822a\u7a7a\u6a5f\u7ffc\u578b\u306e\u5206\u6790<br>\u7cf8\u6c38\u606d\u5e73, \u85ac\u5e2b\u5bfa\u3000\u7fd4, \u5927\u5c71\u3000\u8056, \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, Vol.113, No.111, NC2013-3, pp. 35-40, 2013<\/li>\r\n<li>\u30c6\u30f3\u30bd\u30eb\u5206\u89e3\u578b\u81ea\u5df1\u7d44\u7e54\u5316\u5199\u50cf\u306b\u3088\u308b\u30bd\u30fc\u30b7\u30e3\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u89e3\u6790<br>\u6a4b\u672c \u6643\u4e8c, \u5ca9\u5d0e \u4e98, \u53e4\u5ddd \u5fb9\u751f<br>NC, \u4fe1\u5b66\u6280\u5831, 112, 389(NLP2012 104-143), pp.37-42, 2013<\/li>\r\n<li>\u89e3\u306e\u591a\u91cd\u5ea6\u3092\u8003\u616e\u3057\u305f\u6df7\u5408\u5206\u5e03\u30e2\u30c7\u30eb\u306f\u3088\u308a\u826f\u3044\u89e3\u3092\u3082\u305f\u3089\u3059<br>IBIS, \u4fe1\u5b66\u6280\u5831 112(279), 395-402, 2012<\/li>\r\n<li>\u30c6\u30f3\u30bd\u30eb\u5206\u89e3\u578b\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u306e\u5fdc\u7528: \u6b20\u640d\u30c7\u30fc\u30bf\u88dc\u9593\u65b9\u6cd5\u306e\u63d0\u6848<br>\u5ca9\u5d0e \u4e98, \u53e4\u5ddd \u5fb9\u751f<br>NC, \u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u6280\u8853\u7814\u7a76\u5831\u544a, \u4fe1\u5b66\u6280\u5831 112(227), 55-60, 2012<\/li>\r\n<li>\u975e\u7dda\u5f62\u30c6\u30f3\u30bd\u30eb\u5206\u89e3\u306b\u3088\u308b\u96a0\u308c\u30c0\u30a4\u30ca\u30df\u30ab\u30eb\u30b7\u30b9\u30c6\u30e0\u7a7a\u9593\u63a8\u5b9a<br>Latent Dynamics\u7814\u7a76\u4f1a, pp.12-13, 2012<\/li>\r\n<li>\u5909\u5206\u8fd1\u4f3c\u3092\u898b\u76f4\u3059\u3068SOM\u3068GTM\u306f\u4e00\u5143\u7684\u306b\u7406\u89e3\u3067\u304d\u308b<br>\u677e\u4e0b \u8061\u53f2\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, Vol.111, No.483, pp.257-262, 2012<\/li>\r\n<li>\u6f5c\u5728\u5909\u6570\u5206\u5e03\u3067\u8a55\u4fa1\u3059\u308b\u9ad8\u968e\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7: \u30d8\u30c6\u30ed\u306a\u30c7\u30fc\u30bf\u96c6\u5408\u4f53\u306e\u53ef\u8996\u5316\u30c4\u30fc\u30eb<br>\u77f3\u6a4b \u82f1\u6717\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, Vol.111, No.483, pp.93-98, 2012<\/li>\r\n<li>\u30c6\u30f3\u30bd\u30eb\u5206\u89e3\u578b\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u306e\u958b\u767a: \u975e\u7dda\u5f62\u30c6\u30f3\u30bd\u30eb\u5206\u89e3\u306e\u5b9f\u73fe <br>\u5ca9\u5d0e \u4e98\uff0c \u548c\u7530 \u6c99\u7e54\uff0c \u53e4\u5ddd \u5fb9\u751f<br>NC, Vol.111, No.419, pp.101-106, 2012<\/li>\r\n<li>\u96a0\u308c\u30de\u30eb\u30c1\u30c0\u30a4\u30ca\u30df\u30ab\u30eb\u30b7\u30b9\u30c6\u30e0\u306e\u5b66\u7fd2\u7406\u8ad6\u3068\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0: \u9ad8\u968e\u4f4d\u76f8\u5199\u50cf\u306b\u3088\u308b\u5b9f\u73fe <br>\u53e4\u5ddd \u5fb9\u751f\uff0c \u5927\u4e45\u4fdd \u8cb4\u4e4b<br>NC, Vol.111, No.419, pp.59-64, 2012<\/li>\r\n<li>bayesian optimization makes GTM resemble to SOM<br>Matsushita, S., Furukawa, T.<br>JNNS2011, pp.182-183, 2011<\/li>\r\n<li>SOM canonica: establishing a standard algorithm of self-organizing maps<br>Nakano, M., Ohkubo, T., Furukawa, T.<br>JNNS2011, pp.178-179, 2011<\/li>\r\n<li>another-SOM2 for metrics map: a self-referable neural network<br>Ishibashi, H., Yoneda, K., Furukawa, T.<br>JNNS2011, pp.180-181, 2011<\/li>\r\n<li>what is required for a multi-dynamical system learning task?<br>Ohkubo, T., Furukawa, T.<br>JNNS2011, pp.114-115, 2011<\/li>\r\n<li>\u30c8\u30dd\u30ed\u30b8\u30fc\u5206\u985e\u306e\u305f\u3081\u306e\u9ad8\u968e\u51e6\u7406\u306e\u958b\u767a<br>\u6771 \u7950\u4ecb\uff0c \u53e4\u5ddd \u5fb9\u751f<br>SOFT, pp.59-60, 2011<\/li>\r\n<li>\u6df7\u5408\u30ac\u30a6\u30b9\u30e2\u30c7\u30eb\u3068\u81ea\u5df1\u7d44\u7e54\u5316\u30de\u30c3\u30d7\u306b\u3088\u308b\u968e\u5c64\u60c5\u5831\u51e6\u7406<br>\u4e00\u30ce\u702c \u88d5\u4ecb\uff0c \u53e4\u5ddd \u5fb9\u751f<br>SOFT, pp.55-58, 2011<\/li>\r\n<li>\u6559\u5e2b\u3042\u308aSOM\u306b\u3088\u308b\u30e1\u30c8\u30ea\u30af\u30b9\u5b66\u7fd2<br>\u6e21\u9089 \u9686\u4e4b\uff0c \u53e4\u5ddd \u5fb9\u751f<br>SOFT, pp.53-54, 2011<\/li>\r\n<li>\u78ba\u7387\u7684\u751f\u6210\u30e2\u30c7\u30eb\u306b\u3088\u308b\u4f4d\u76f8\u4fdd\u5b58\u5199\u50cf\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u5c0e\u51fa<br>\u677e\u4e0b \u8061\u53f2\uff0c \u4e2d\u91ce \u5c06\u79c0\uff0c \u53e4\u5ddd \u5fb9\u751f<br>IBIS2011, 2011<\/li>\r\n<li>\u4f4d\u76f8\u4fdd\u5b58\u5199\u50cf\u3092\u7528\u3044\u305f\u95a2\u4fc2\u30c7\u30fc\u30bf\u306e\u975e\u7dda\u5f62\u30c6\u30f3\u30bd\u30eb\u5206\u89e3<br>\u5ca9\u5d0e \u4e98\uff0c \u548c\u7530 \u6c99\u7e54\uff0c \u53e4\u5ddd \u5fb9\u751f<br>IBIS2011, 2011<\/li>\r\n<li>\u4f4d\u76f8\u4fdd\u5b58\u5199\u50cf\u306e\u6a19\u6e96\u7406\u8ad6\u78ba\u7acb\u306e\u8a66\u307f<br>\u53e4\u5ddd \u5fb9\u751f<br>NC, Vol.111, No.241, pp.101-106, 2011<\/li>\r\n<\/ul>\r\n<p class=\"pagetop\"><a href=\"#\">\u25b3\u30da\u30fc\u30b8\u30c8\u30c3\u30d7\u3078\u623b\u308b<\/a><\/p>\r\n<\/section>","protected":false},"excerpt":{"rendered":"Jornal Paper Visual analytics of set data for knowledge discovery and member selection supportRyuji Watanabe,  [&hellip;]","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-61","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/pages\/61","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/comments?post=61"}],"version-history":[{"count":0,"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/pages\/61\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.brain.kyutech.ac.jp\/~furukawa\/wp-json\/wp\/v2\/media?parent=61"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}