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We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains,...
Conventional k-means only considers pair wise similarity during cluster assignment, which aims to minimizing the distance of points to their nearest cluster centroids. In high dimensional space like document datasets, however, two points may be nearest neighbors without belonging to the same class. Thus pair wise similarity alone is often insufficient for class prediction in such space. To that end,...
Fraud is increasing with the extensive use of internet and the increase of online transactions. More advanced solutions are desired to protect financial service companies and credit card holders from constantly evolving online fraud attacks. The main objective of this paper is to construct an efficient fraud detection system which is adaptive to the behavior changes by combining classification and...
The classification of a large number of images is a familiar problem to the image processing community. It occurs in consumer photography, bioinformatics, biomedical imaging, surveillance, and in the field of mobile eye-tracking studies. During eye-tracking studies, what a person looks at is recorded, and for each frame what the person looked at must then be analyzed and classified. In many cases...
This paper is for text categorization of Enron email corpus, we use the information bottleneck (IB) method to cluster the key words based on their distribution on different class labels, then we use threads and address groups as additional features to email texts, and the maximal entropy model to improve the accuracy of the classifier. Our experimental results shows that these measures can improve...
Data mining is an automated process of discovering knowledge from databases. There are various kinds of data mining methods aiming to search for different kinds of knowledge. Data mining systems induce knowledge from data sets, which are huge, noisy (incorrect), incomplete, inconsistent, imprecise (fuzzy), and uncertain. The problem is that existing systems use a limiting attribute value language...
This paper addresses an important and vital problem within the general area of disease recognition, namely identifying disease biomarker genes. Given the complexity of this domain, the basic idea tacked in this paper is employing multiple agents to handle this problem. Though the developed methodology is general enough to be applied to any other domain, we concentrate on identifying cancer biomarkers...
Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized...
Entropy model is the base structure of automated word categorizing. In this model, words appear consecutive frequently will place in different groups. Although this method is not correct always, in the most cases, obtained results simulate real situation. Because of NP-complete structure of clustering problems, the entropy model cannot be solved by an optimal algorithm, so a number of heuristic algorithms...
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