in different situations. The results reveal that a new feature selection metric we call ‘Bi-Normal Separation ’ (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text classification problems and is particularly

Context is a poorly used source of information in our computing environments. As a result, we have an impoverished understanding of what context is and how it can be used. In this paper, we provide an operational definition of context and discuss the different ways that context can be used

The paper develops a new theory for the analysis of games with incomplete information where the players are uncertain about some important parameters of the game situation, such as the payoff functions, the strategies available to various players, the information other players have about the game

-state representation. It is proposed that different types of self-discrepancies represent different types of negative psychological situations that are associated with different kinds of discomfort. Discrepan-cies between the actual/own self-state (i.e., the self-concept) and ideal self-stales (i.e., representations

The ideas of information theory are at present stimulating many different areas of psychological inquiry. In providing techniques for quantifying situations which have hitherto been difficult or impossible to quantify, they suggest new and more precise ways of conceptualizing these situations (see

that factors from skill situa-tions (competition, choice, familiarity, involvement) introduced into chance situations cause individuals to feel inappropriately confident. In Study 1 sub-jects cut cards against either a confident or a nervous competitor; in Study 2 lottery participants were or were not given a

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment

Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples

applicable in path-dependent and multifactor situations where traditional finite difference techniques cannot be used. We illustrate this technique with several realistic exatnples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an America11 swaption in a 20

error). Two widely used statistical tests are shown to have high probability of type I error in certain situations and should never be used: a test for the difference of two proportions and a paired-differences t test based on taking several random train-test splits. A third test, a paired-differences