Information retrieval is the process of evaluating a user's query, or information need, against a set of documents (books, journal articles, web pages, etc.) to determine which of the documents satisses the query. With the advent of the World Wide Web, there is suddenly a need to query enormous sets of documents both eeciently and accurately. In the vector space model of information retrieval, ...

The propensity score method is frequently used to deal with bias from standard regression in observational studies. The propensity score method involves calculating the conditional probability (propensity) of being in the treated group (of the exposure) given a set of covariates, weighting (or sampling) the data based on these propensity scores, and then analyzing the outcome using the weighted...

In this article we report on a study of how to use the context tree weight-ing (CTW) algorithm for lossless image compression. This algorithm has been shown to perform optimally, in terms of redundancy, for a wide class of data sources. Our study shows that this algorithm can successfully be applied to image compression even in its basic form. We also report on possible modiica-tions of the bas...

We consider a class of random point and germ-grain processes, obtained using a rather natural weighting procedure. Given a Poisson point process, on each point one places a grain, a (possibly random) compact convex set. Let be the union of all grains. One can now construct new processes whose density is derived from an exponential of a linear combination of quermass functionals of. If only the ...

In this article, we consider the problem of computing minimum dominating set for a given set S of n points in R. Here the objective is to find a minimum cardinality subset S of S such that the union of the unit radius disks centered at the points in S covers all the points in S. We first propose a simple 4-factor and 3-factor approximation algorithms in O(n log n) and O(n logn) time respectivel...

We present Nonstandard Analysis by three axioms: the Extension, Transfer and Saturation Principles in the framework of the superstructure of a given innnite set. We also present several applications of this axiomatic approach to point-set topology. Some of the topological topics such as the Hewitt realcompactiication and the nonstandard characterization of the sober spaces seem to be new in the...

Feature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting...

Text categorization is a task of automatically assigning documents to a set of predefined categories. Usually it involves a document representation method and term weighting scheme. This paper proposes a new term weighting scheme called Modified Inverse Document Frequency (MIDF) to improve the performance of text categorization. The document represented in MIDF is trained using the support vect...

This paper examines the realization of acausal weighting patterns with two-point boundary-value descriptor systems (TPBVDSs). We restrict our attention to the subclass of TPBVDSs which are extendible, i.e., whose input-output weighting pattern can be extended outwards indefinitely, and stationary, so that their weighting pattern is shift-invariant. Then, given an infinite acausal shift-invarian...