Earticle

초록

영어

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

목차

I. Introduction
 II. Overview of the ExOM
  2.1 Schema Definition
  2.2 Queries and Operators
 III. Learning and Classification Components
  3.1 Categories
  3.2 Classifier
  3.3 Learner
 IV. Consistency of ExOM Databases
 V. Prototype Implementation
 VI. Related Work
 VII. Conclusion
 References

저자

• 김용재 [ Yong Jae Kim | Authors are affiliated with Colege of Business Administration, Konkuk University ]
• 안준모 [ Joon M. An | College of Business Administration, Konkuk University ] Corresponding author

참고문헌