Browsing by Author "Nadeem Ahmed SYED"

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    From Incremental Learning to Model Independent Instance Selection - A Support Vector Machine Approach
    (1999-09-01T00:00:00Z) Nadeem Ahmed SYED; Huan LIU; Kah Kay SUNG
    With large amounts of data being available to machine learning community, the need to design techniques that scale well is more critical than ever before. As some data may be collected over long periods, there is also a continuous need to incorporate the new data into the previously learned concept. Incremental learning techniques can satisfy the need for both the scalability and incremental update. In this paper, we categorize the incremental techniques into two broad categories: block by block vs instance by instance. We suggest three criteria to evaluate the robustness and reliability of incremental learning methods. We then propose an incremental learning method for Support Vector Machines, and use the suggested criteria to evaluate the effectiveness of the suggested training method. Motivated by positive results on these experiments, we research the possibility of using SVMs for another approach to handling very large datasets. We have carried out a study to evaluate whether the Support Vector Machine (SVM) training can be used to select a small subset of examples from the training set in a model independent way. We compare the results of SVM selection, with IB2 selection method and random sampling. We analyze the experiment results, and discuss their implications. All the results have been illustrated using standard machine learning benchmark datasets.