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Sparsity enforcing edge detection method for blurred and noisy Fourier data



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Online Microarray Analysis Tool using a modified support vector machine MSVM An internship report for CBS MS Degree Committee Dr Rosemary Renaut1 Professor Department of Mathematics and Statistics Director Computational Biosciences PSM Arizona State University Dr Huan Liu2 Professor Department of Computer Science Engineering Arizona State University Dr Hongbin Guo3 Post doctoral Fellow Department of Mathematics and Statistics Arizona State University Student Wang Juh Chen Sting 4 Computational Bioscience PSM Arizona State University May 9 2005 Report Number 05 03 1 email renaut asu edu 2 email Huan Liu asu edu 3 email hb guo asu edu 4 email wang juh chen asu edu 1 Abstract Microarray is becoming an important tool for monitoring and analyzing gene expression profiles from thousands of genes simultaneously Due to the characteristic of these datasets where the dimension of the feature space is far greater than the sample size we can not use traditional methods for information retrieval Researchers are looking for some other tools to solve this problem such as supervised machine learning and data mining Supervised machine learning and data mining tools are popular for the analysis of gene expression microarray data The Support Vector Machine SVM is one of these For the non separable case SVM introduces slack variables in mapped space as measurements of misclassifications the source of which may be mislabeling error of data or and outliers Here we investigate a novel way to approach the misclassifications We account for the misclassifications in feature space by an errors in data approach based on the observation of the noisy characteristic of microarray data In this internship we propose a SVM as a classifier in which the measurement errors are incorporated in the feature space During the training phase involving huge datasets with large number of features but actual small sample size it will cost a lot of time and memory so we will introduce a CLUSTER platform ROCKS to



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