Automated generation of model-based knowledge acquisition tools
Automated generation of model-based knowledge acquisition tools
Collision: theory vs. reality in expert systems (2nd ed.)
Collision: theory vs. reality in expert systems (2nd ed.)
A philosophical basis for knowledge acquisition
Knowledge Acquisition
Knowledge acquisition as a constructive modeling activity
Knowledge acquisition as modeling
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Automated Knowledge Acquisition for Strategic Knowledge
Machine Learning
Knowledge Acquisition without Analysis
Proceedings of the 7th European Workshop on Knowledge Acquisition for Knowledge-Based Systems
Incremental context mining for adaptive document classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Machine learning methods are generally employed to acquire the knowledge for automated document classification. They can be used if a large set of pre-sampled training set is available and the domain does not change rapidly. However, it is not easy to get a complete trained data set in the real world. Furthermore, the classification knowledge continually changes in different situations. This is known as the maintenance problem or knowledge acquisition bottleneck problem. Multiple Classification Ripple-Down Rules (MCRDR), an incremental knowledge acquisition method, was introduced to resolve this problem and has been applied in several commercial expert systems and a document classification system. Evaluation results for several domains show that our MCRDR based document classification method can be successfully applied in the real world document classification task.