a r t i c l e i n f o a b s t r a c t The method based on Bag-of-visual-Words (BoW) deriving from local keypoints has recently appeared promising for video annotation. Visual word weighting scheme has critical impact to the performance of BoW method. In this paper, we propose a new visual word weighting scheme which is referred as emerging patterns weighting (EP-weighting). The EP-weighting sch...

Text Categorization is the process of automatically assigning predefined categories to free text documents. Feature weighting, which calculates feature (term) values in documents, is one of important preprocessing techniques in text categorization. This paper is a comparative study of feature weighting methods in statistical learning of Thai Document Categorization Framework. Six methods were e...

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to ha...

Weighting is the term most frequently used to describe magnetic resonance pulse sequences and the concept most commonly used to relate image contrast to differences in magnetic resonance tissue properties. It is generally used in a qualitative sense with the single tissue property thought to be most responsible for the contrast used to describe the weighting of the image as a whole. This articl...

One of the surprising findings from the study of CNF satisfiability in the 1990’s has been the success of iterative repair techniques, and in particular of weighted iterative repair. However, attempts to improve weighted iterative repair have either produced marginal benefits or rely on domain specific heuristics. This paper introduces a new extension of constraint weighting called Arc Weightin...

A probability weighting function w(p) for an objective probability p in decision under risk plays a pivotal role in Kahneman-Tversky’s prospect theory. Although recent studies in econophysics and neuroeconomics widely utilized probability weighting functions, psychophysical foundations of the probability weighting functions have been unknown. Notably, a behavioral economist Prelec (1998) axioma...

Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles typically fall into instance-based, cluster-based, or hybrid approaches; however, most of such methods fail in discriminating among the various clusterings that participate to the ensemble. In this paper, we address th...

PURPOSE Energy-resolved CT has the potential to improve the contrast-to-noise ratio (CNR) through optimal weighting of photons detected in energy bins. In general, optimal weighting gives higher weight to the lower energy photons that contain the most contrast information. However, low-energy photons are generally most corrupted by scatter and spectrum tailing, an effect caused by the limited e...

In this paper a hierarchical classification framework using the feature-weighting tree for the objective of applying diverse weighting to acoustic features is proposed for speech recognition. The hierarchical feature-weighting tree with a flexible structure complexity can be constructed optimally with the optimal splitting for the recognition confusion graph. Based on the minimum classification...

Performance of an information retrieval system depends on its weighting scheme. Weighting of a term can be seen in two aspects, local and global. For each type of weighting scheme, generally, single terms are considered. Term dependency is quite natural in a document. Word pairs or phrases can better describe a document in place of single terms. In the present paper an attempt has been made to ...