Amit Mandvikar

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Due to the digitization of data and advances in technology, it has become extremely easy to obtain and store large quantities of data, particularly Multimedia data. Fields ranging from Commercial to Military need to analyze these data in an efficient and fast manner. Presently, tools for mining images are few and require human intervention. Feature(More)
The Teaching Undergraduates Data Mining in Engineering Programs project was supported by the National Science Foundation. The project was jointly executed between universities. The project objectives were to: (1) develop an undergraduate data mining course that could be taught in semester or quarter systems and within institutions of varying demographics,(More)
In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant(More)
This chapter focuses on the development of an active learning approach to an image mining problem for detecting Egeria densa (a Brazilian waterweed) in digital imagery. An effective way of automatic image classification is to employ learning systems. However, due to a large number of images, it is often impractical to manually create labeled data for(More)
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