In machine learning problems a learning algorithm tries to learn the input–output dependency (relationship) of a system from a training dataset. This input–output relationship is usually deformed by a random noise. From experience, simulations, and special case theories, most practitioners...

In the problem of binary classification (or medical diagnosis), the classification rule (or diagnostic test) produces a continuous decision variable which is compared to a critical value (or threshold). Test values above (or below) that threshold are called positive (or negative) for disease....

The problem of determining the size of the training sample needed to achieve sufficiently small misclassification probability is considered. The appropriate sample size is approximated using a stopping rule. The proposed procedure is asymptotically optimal.

A Central Limit Theorem for a triangular array of row-wise independent Hilbert-valued random elements with finite second moment is proved under mild convergence requirements on the covariances of the row sums and the Lindeberg condition along the evaluations at an orthonormal basis. A Central...

Non-homogeneous Poisson Process (NHPP) models form a significant subclass of the many software reliability models proposed in the literature. We prove an important limitation of NHPP models for which the expected number of failures in infinite testing is finite. Specifically, the parameters of...

Several studies on gender performance reveal that enterprises run by males generally outperform those run by females. This article examines the performance differences between male and female entrepreneurs in the informal service sector in India, using the NSSO (National Sample Survey...

Introduction -- Bank Nationalization: Background and Formulation -- Review of Literature -- Issues in Data Selection and Description of Data -- Conventional Analysis of Banks' Performance -- Measurement of Competition -- Measuring of Banks' Performance by Using Super-Efficiency DEA Model --...