In this paper, we explore the bias of term weighting schemes used by retrieval models. Here, we consider bias as the extent to which a retrieval model unduly favours certain documents over others because of characteristics within and about the document. We set out to find the least biased retrieval model/weighting. This is largely motivated by the recent proposal of a new suite of retrieval mod...

MOTIVATION Large alignments of ribosomal RNA sequences are maintained at various sites. New sequences are added to these alignments using a combination of manual and automatic methods. We examine the use of profile alignment methods for rRNA alignment and try to optimize the choice of parameters and sequence weights. RESULTS Using a large alignment of eukaryotic SSU rRNA sequences as a test c...

In this paper, an improved web page classification method (IWPCM) using neural networks to identify the illicit contents of web pages is proposed. The proposed IWPCM approach is based on the improvement of feature selection of the web pages using class based feature vectors (CPBF). The CPBF feature selection approach has been calculated by considering the important term's weight for illicit web...

We investigate different areas of the highdimensional vector space built by the automatic text summarizer HolSum, which evaluates sets of summary candidates using their similarity to the original text. Previously, the search for a good summary was constrained to a very limited area of the summary space. Since an exhaustive search is not reasonable we have sampled new parts of the space using ra...

The nearest neighbor classifier is a powerful, straightforward, and very popular approach to solving many classification problems. It also enables users to easily incorporate weights of training instances into its model, allowing users to highlight more promising examples. Instance weighting schemes proposed to date were based either on attribute values or external knowledge. In this paper, we ...

Previous work on feature weighting for case-based learning algorithms has tended to use either global weights or weights that vary over extremely local regions of the case space. This paper examines the use of coarsely local weighting schemes, where feature weights are allowed to vary but are identical for groups or clusters of cases. We present a new technique, called class distribution weight...

Optical character recognition (OCR) is the most commonly used technique to convert printed material into electronic form. Using OCR, large repositories of machine readable text can be created in a short time. An information retrieval system can then be used to search through large information bases thus created. Many information retrieval systems use sophisticated term weighting functions to im...

Time of arrival (TOA) based geolocation schemes for indoor multi-hop environment are investigated and compared to some of conventional geolocation schemes such as least squares (LS) or residual weighting (RWGH). The multi-hop ranging involves positive multi-hop noise as well as non-line of sight (NLOS) and Gaussian measurement noise, so that it is more prone to ranging error than one-hop range....

This paper explores a relationship between parameters for the context tree weighting and weights for a general model weighting technique. In particular, an algorithm is proposed that approximately computes the parameters from the weights, and a condition under which no error for the approximation occurs is derived. key words: model weighting technique, tree source, context tree weighting.

We compare the techniques of voting and stacking for system combination in datadriven dependency parsing, using a set of eight different transition-based parsers as component systems. Experimental results show that both methods lead to significant improvements over the best component system, and that voting gives the highest overall accuracy. We also investigate different weighting schemes for ...