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Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the removal of collinear and irrelevant variables for cre...

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM...

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popul...

This paper presents a system that can take short messages relevant to a particular topic from a microblogging service such as Twitter or Facebook, analyze the messages for the sentiments they carry on, and classify them....

MOTIVATION: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missi...