The development of knowledge-based systems relies heavily on the transfer of human expertise into a structured knowledge base - a process known as knowledge acquisition or knowledge elicitation. This process is widely ...

The primary objective of this thesis is to develop a methodology for clustering of objects based on their functionality, typified by the notion of concept. We begin by giving a formal definition of concept. By assigning a ...

Clustering is a process of grouping a collection of objects. Clustering approaches can be broadly categorized into conventional and knowledge-based approaches. In a conventional approach, objects are typically represented ...

Precise specification of the architecture and design of software is a good practice. Such specifications contain a lot of information about the software that can potentially be exploited by tools to reduce redundancy in ...

Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are ...

Large sparse matrices arise in many applications, especially in the major problems of Cryptography of factoring integers and computing discrete logarithms. We focus attention on such matrices called sieve matrices generated ...

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 ...

Deductive verification techniques in the style of Floyd and Hoare have the potential to give us concise, compositional, and scalable proofs of the correctness of various kinds of software systems like programs and control ...

Machine Learning(ML) for Systems is a new and promising research area where performance of computer systems is optimized using machine learning methods. ML for Systems has outperformed traditional heuristics methods in ...