Ding-Yi Chen

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Effective decision making is based on accurate and timely information. However, human decision makers are often overwhelmed by the huge amount of electronic data these days. The main contribution of this paper is the development of effective information agents which can autonomously classify and filter incoming electronic data on behalf of their human(More)
We describe JGAP, a web-based platform for designing and implementing Java-coded graph algorithms. The platform contains a library of common data structures for implementing graph algorithms, features a ‘plug-and-play’ modular design for adding new algorithm modules, and includes a performance meter to measure the execution time of implemented algorithms.(More)
AbstrAct In this paper, we propose a framework namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling the misclassified documents. Whenever a user points out misclas-sified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces(More)
Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email(More)
Given a large volume of Web documents, we consider problem of finding the shortest keyword sequences for each of the documents such that a keyword sequence can be rendered to a given search engine, then the corresponding Web document can be identified and is ranked at the first place within the results. We call this system as an Inverse Search Engine (ISE).(More)