CONTENTS<\/p>\n
Commentary
\nAI Attacks Back\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Koichi Hori 749<\/p>\n
Special Issue:
\n\u201cComparison and Evaluation of KDD Methods with Common Medical Datasets\u201d<\/p>\n
Editor\u2019s Introduction to
\n\u201cComparison and Evaluation of KDD Methods with Common Medical Datasets\u201d
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Takahira Yamaguchi 750<\/p>\n
The Common Medical Data Sets to Compare and Evaluate KDD Methods
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Shusaku Tsumoto 751<\/p>\n
Extention of Association Rule Mining for Structured and Numerical Data
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Takashi Washio and Hiroshi Motoda 759<\/p>\n
Knowledge Discovery from Common Data Sets Using an Automatic
\nComposition Tool for Inductive Applications
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Takahira Yamaguchi 768<\/p>\n
Rule Discovery in Medical Data by GDT-RS
\n\u2026\u2026\u2026\u2026\u2026\u2026..Ning Zhong, Juzhen Dong and Setsuo Ohsuga 774<\/p>\n
Hypothesis-Driven Exception-Rule Discovery from Common Data Sets
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Einoshin Suzuki 782<\/p>\n
Comparison and Evaluation of Knowledge Obtained by KDD Methods
\n\u2026\u2026\u2026\u2026\u2026\u2026..Shusaku Tsumoto and Katsuhiko Takabayashi 790<\/p>\n
Survey Papers
\nThe RoboCup-Rescue Project\u2026Satoshi Tadokoro, Tomoichi Takahashi,
\nHironao Takahashi, Michinori Hatayama, Fumitoshi Matsuno,
\nMasayuki Ohta, Tetsuhiko Koto, Ikuo Takeuchi, Takeshi Matsui,
\nYoshitaka Kuwata, Toshiyuki Kaneda, Masayasu Atsumi,
\nJun Nobe and Hiroaki Kitano 798<\/p>\n
RoboCup-Rescue: Challenge to Rescue in Large-Scale Disasters
\n\u2026\u2026\u2026.Satoshi Tadokoro, Hisanori Amano, Yoshitaka Kuwata,
\nHiroaki Kitano, Ikuo Takeuchi and Tomoichi Takahashi 807<\/p>\n
Serial Survey Papers:
\nIndustrial Applications of Artificial Intelligence Technology (10)
\nField Quality Watchdog System \u2014 A Data Mining Application \u2014
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Satoshi Hori, Miki Ochida,
\nYoshihiro Hamada and Kazushige Imura 813<\/p>\n
AI map
\nUnderstanding Natural Language \u2014 Beyond SHRDLU \u2014
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Hozumi Tanaka 821<\/p>\n
Technical Papers
\nMacro-Model Generation for Emergent Cooperative Behaviors
\nin Ant Colony\u2019s Foraging (1) \u2014 A Simple Model Case \u2014
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..Koichi Kurumatani 829<\/p>\n
Macro-Model Generation for Emergent Cooperative Behaviors
\nin Ant Colony\u2019s Foraging (2)
\n$B!!(B \u2014 Decentralized Control by Desensitization Mechanism \u2014
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..Koichi Kurumatani 837<\/p>\n
Analog Evolvable Hardware Using Variable Length Chromosomes
\n\u2026\u2026\u2026\u2026\u2026\u2026Shin Ando, Mitsuru Ishizuka and Hitoshi Iba 844<\/p>\n
Effects of Degree of Shared Cognitive Space
\non Collaborative Discovery Processes\u2026\u2026\u2026\u2026..Kazuhisa Miwa 854<\/p>\n
Knowledge Acquisition in Database by Generating Negative Instances
\nBased on Similarity between Instances in Database
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Yuu Morinaka, Kouzou Ohara,
\nNoboru Babaguchi and Tadahiro Kitahashi 862<\/p>\n
Distance Calculation Method for Speakers
\nBased on a Structure of Hidden Markov Network
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..Motoyuki Suzuki and Shozo Makino 871<\/p>\n
Extraction of Primitive Motions by Using Clustering and
\nSegmentation of Motion-Captured Data
\n\u2026\u2026\u2026\u2026.Ryuta Osaki, Mitsuomi Shimada and Kuniaki Uehara 878<\/p>\n
A Study of a Probabilistic Model for a Figure Detection System to
\nConstruct Image Processing Procedures Automatically
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..Toshihiro Hamada, Akinobu Shimizu,
\nToyofumi Saito, Jun-ichi Hasegawa and Jun-ichiro Toriwaki 887<\/p>\n
Emergence of Norms of Behavior in a Competitive Social Model
\n\u2026\u2026\u2026..Takashi Ishida, Hiroshi Yokoi and Yukinori Kakazu 896<\/p>\n
An Intelligent System for Reconstruction
\nof Curved-Surfaced Solids from Inconsistent Three Views
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..Yuyan Chao and Takashi Watanabe 907<\/p>\n
Fraud-Free Exchange Mechanisms in Electronic Commerce
\n\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Shigeo Matsubara and Makoto Yokoo 915<\/p>\n
Published Bimonthly by Japanese Society for Artificial Intelligence
\nOS Bldg.402,4-7 Tsukudomachi,Shinjuku-ku,Tokyo 162-0821,Japan.<\/p>\n
Last modified: Mon Aug 21 21:28:35 JST 2000<\/p>\n","protected":false},"excerpt":{"rendered":"
CONTENTS Commentary AI Attacks Back\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026Koichi Hori 749 Special Issue: \u201cComparison and Evaluation of KDD Methods with Common Medical Datasets\u201d Editor\u2019s Introduction to \u201cComparison and Evaluation of KDD Methods with Common Medical Datasets\u201d \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Takahira Yamaguchi 750 The Common Medical Data Sets to Compare and Evaluate KDD Methods \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Shusaku Tsumoto 751 Extention of Association Rule Mining for Structured and Numerical Data \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Takashi Washio and Hiroshi Motoda 759 Knowledge Discovery from Common Data Sets Using an Automatic Composition Tool for Inductive Applications \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Takahira Yamaguchi 768 Rule Discovery in Medical Data by GDT-RS \u2026\u2026\u2026\u2026\u2026\u2026..Ning Zhong, Juzhen Dong and Setsuo Ohsuga 774 Hypothesis-Driven Exception-Rule Discovery from Common Data Sets \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.Einoshin Suzuki 782 Comparison and Evaluation […]<\/p>\n","protected":false},"author":17,"featured_media":0,"parent":1007049,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":[],"categories":[11],"_links":{"self":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1000297"}],"collection":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/comments?post=1000297"}],"version-history":[{"count":3,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1000297\/revisions"}],"predecessor-version":[{"id":1008397,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1000297\/revisions\/1008397"}],"up":[{"embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/pages\/1007049"}],"wp:attachment":[{"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/media?parent=1000297"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ai-gakkai.or.jp\/en\/wp-json\/wp\/v2\/categories?post=1000297"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}