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We propose a convex optimization based strategy to deal with uncertainty in the observations of a classification problem. We assume that instead of a sample (xi, yi) a distributio...
Chiranjib Bhattacharyya, Pannagadatta K. Shivaswam...
Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., ...
Classification problems are traditionally focused on uniclass samples, that is, each sample of the training and test sets has one unique label, which is the target of the classific...
In this paper the Karnaugh and Quine-McCluskey methods are used for symbolic classification problem, and then these methods are compared with other famous available methods. Becau...
Abstract: Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can b...
—Packet classification problem has received much attention and continued to be an important topic in recent years. In packet classification problem, each incoming packet should b...
In solving the classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets sto...