Support Vector Machines (SVM) have been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy...

This paper proposes a parameter-free classifier which combines K-means with Nearest Neighbor Rule (NNR) called Incremental Cluster-based Classification (ICC). The classifier is used in low power and capacity devices such as Personal Digital Assistant (PDA) and Smartphone. In the training phase, ICC employs K-means to group instances into several clusters, and then incrementally separates the cl...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FAST SVM TRAINING USING APPROXIMATE EXTREME POINTS By Manu Nandan December 2013 Chair: Pramod Khargonekar Cochair: Sachin Talathi Major: Computer Engineering Support vectors machines (SVMs) are widely applied machine learning algorithms ...

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum m...

Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised lear...

INTRODUCTION Pattern classification techniques offer a novel way of looking at imaging data. Support vector classification is a machine learning technique that has been used to perform classification of fMRI data [1]. Brain state classification may not always be a binary classification problem, as the level of BOLD activation may be a graded response [2]. This study explores support vector regr...

OBJECTIVE For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence pre...

This thesis encompasses parts of the field of computer vision. The main problem dealt with throughout this project is how to automate classification of specific human activities via video streams. The essence of this project is therefore that the developed algorithm shall be able to distinguish these activities from one another. A number of restrictions were imposed on the data-set in order to ...

Probabilistic Support Vector Machine Classification (PSVC) is a real time detection and prediction algorithm that is used to overcome assumptions regarding the distribution of the data. Its classification output is complemented with a probabilistic cost function and incorporates a degree of uncertainty in its predictions. Computationally, this algorithm is fast because it performs the classific...