Support vector machine (SVM) classifiers represent one of the most powerful and promising tools for solving classification problems. In the past decade SVMs have been shown to have excellent performance in the field of data mining. The standard SVM classifier treats all instances equally. However, in many applications we have different levels of confidence in different instances that belong to ...

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, ...

This paper describes three weighting schemes for improving the accuracy of progressive multiple sequence alignment methods: (1) global profile pre-processing, to capture for each sequence information about other sequences in a profile before the actual multiple alignment takes place; (2) local pre-processing; which incorporates a new protocol to only use non-overlapping local sequence regions t...

The majority of the algorithms in the clustering literature utilize data sets with numerical values. Recently, new and scalable algorithms have been proposed to cluster data sets with categorical data, data whose inherent ordering is not obvious. However, these algorithms deem all data values present in the data sets as equally important. Thus, the resulting clusters may be influenced by values...

A method for capturing high intensity dynamic range scenes with a low dynamic range camera consists in taking a series of images with di erent exposure settings and combining these into a single high dynamic range image. The combined image values are found by weighted averaging of values from the di erently exposed images on a per-pixel basis. This paper reviews existing weighting schemes and c...

J. Lee, A. L. Alexander Medical Physics, University of Wisconsin, Madison, WI, United States Background/Introduction Diffusion tensor MRI and the associated measures, such as FA, trace {D}, and eigenvector direction, are highly sensitive to image measurement noise. The main strategy to decrease noise sensitivity is to employ uniformly distributed diffusion encoding directions with a diffusion-w...

Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating func...