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Classical attribute-value descriptions induce a multi-dimensional geometric space. One way for computing the distance between descriptions in such a space consists in evaluating an Euclidean distance between tuples of coordinates. This is the ground on which a large part of the Machine Learning literature has built its methods and techniques. However, the complexity of some domains require the use...
The ultimate goal of knowledge discovery (KD) is to extract sets of patterns leading to useful knowledge for obtaining user desirable outcomes. The key characteristics of knowledge usefulness is that these patterns are actionable. In the last decade, KD algorithms such as mining for association rules, clustering, and classification rules, have made a tremendous progress and have been demonstrated...
Semantic concept learning is one of the most challenging problems in video retrieval. The key barrier for semantic concept learning is lack of annotated training data. Internet videos are different from ordinary videos: massive, rich information, customized, non-uniform format, uneven quality, little descriptive text, only a few shots with limited length etc. Therefore, Internet is a potential repository...
This paper focuses on developing classification algorithms for problems in which there is a need to predict the class based on multiple observations (examples) of the same phenomenon (class). These problems give rise to a new classification problem, referred to as set classification, that requires the prediction of a set of instances given the prior knowledge that all the instances of the set belong...
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