Autonomic management is important in storage systems and the space of autonomics in storage systems is vast. Such autonomic management systems can employ a variety of techniques depending upon the specific problem. In this ...

The central theme of the thesis is to study linear and non linear SVM formulations in the presence of uncertain observations. The main contribution of this thesis is to derive robust classfiers from partial knowledge of ...

The problem of identifying clusters arising in the context of topic models and related approaches is important in the area of machine learning. The problem concerning traversals on Concept Association Networks is of great ...

Many real-world problems like hand-written digit recognition or semantic scene classification are treated as multiclass or multi-label classification prob-lems. Solutions to these problems using support vector machines (SVMs) ...

Optimization is a very important field with diverse applications in physical, social and biological sciences and in various areas of engineering. It appears widely in ma-chine learning, information retrieval, regression, ...

This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with ...

Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data ...

Multilabel classification has attracted much interest in recent times due to the wide applicability of the problem and the challenges involved in learning a classifier for multilabeled data. A crucial aspect of multilabel ...

Nonnegative matrix factorization (NMF) is an important data-analysis problem which concerns factoring a given d n matrix A with nonnegative entries into matrices B and C where B and C are d k and k n with nonnegative ...