Progress in Discovery Science: Final Report of the Japanese Discovery Science ProjectThis volume contains the research reports of the Discovery Science pro ject in Japan (No. 10143106), in which more than 60 scientists participated. It was a three-year pro ject sponsored by Grant-in-Aid for Scienti?c Research on Priority Areas from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan. This pro ject mainly aimed to (1) develop new methods for knowledge discovery, (2) install network environments for knowledge discovery, and (3) establish Discovery Science as a new area of study in Computer Science / Arti?cial Intelligence. In order to attain these aims we set up ?ve groups for studying the following research areas: (A) Logic for/of Knowledge Discovery (B) Knowledge Discovery by Inference/Reasoning (C) Knowledge Discovery Based on Computational Learning Theory (D) Knowledge Discovery in Huge Databases and Data Mining (E) Knowledge Discovery in Network Environments These research areas and related topics can be regarded as a preliminary d- inition of Discovery Science by enumeration. Thus Discovery Science ranges over philosophy, logic, reasoning, computational learning, and system developments. In addition to these ?ve research groups we organized a steering group for planning, adjustment, and evaluation of the project. The steering group, chaired by the principal investigator of the project, consists of leaders of the ?ve research groups and their subgroups as well as advisors from outside of the pro ject. We invited three scientists to consider Discovery Science and the ?ve above m- tioned research areas from viewpoints of knowledge science, natural language processing, and image processi ng, respectively. |
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Contents
uyuautoIIS Reducing Search Space in Solving HigherOrder Equations | 19 |
From a Philosophical Point of View | 31 |
Ideal Concepts Intuitions and Mathematical Knowledge Acquisitions | 40 |
Copyright | |
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abstraction algorithm analysis applied assume attributes calculated called classified complete component Computer concept condition consider consists construct contains context corresponding database decision tree defined definition denote derivation developed discovered discovery distribution domain efficient equation estimate evaluation example experiments expressions extraction fact Figure formal framework function give given Husserl hypothesis inductive introduced intuition judgment knowledge label language learning logic machine mathematical means measure method mining namely negative node Note notion objects observed obtained operation optimal original parameters pattern performance positive prediction present probability problem Proc proposed propositions References relation represented respectively rules sample Science selected sequence shown shows similar space string structure Table techniques Theorem theory tree true University variables wave