Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image subwindows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ 2 kernels

signal activities. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on the data sets acquired using BC12000's P300 Speller Paradigm provided by BCI competitions II (2003) & III (2004

Abstract | This review provides an introduction to Sup-

Bag-of-words-based image classification approaches mostly rely on low level local shape features. However, it has been shown that combining multiple cues such as color, texture, or shape is a challenging and promising task which can improve the classification accuracy. Most of the state

Abstract—As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor

ii iv vTo my parents vi vii Acknowledgements I would first and foremost extend my whole-hearted gratitude to my advisors, An-drea Arpaci-Dusseau and Remzi Arpaci-Dusseau. Andrea and Remzi are the reason that I had the opportunity for this exceptional Ph.D. journey. To this day, I still re-member the moment when they took me as their student and the joy and hope in my heart. Andrea and Remzi have showed me what systems research is like and how much fun and challenging it can be. Before this journey with them, I had always liked and believed in the beauty of mathematics and theory. My initial interest in systems research happened when I took Remzi’s Advanced Operating Systems

the ultra-fast, optimized Multiple Kernel Learning (UFO-MKL) algorithm; and concatenation-based method where the features are combined into a single feature vector and classified by a linear SVM. Experiments carried out on a recent spontaneous micro-expression database demonstrated the capability

learning based method for pair-wise events detection. Visual features are extracted as a cubic feature representation and the discrimination is based on multiple relational and sequence kernels. Experimental results show that our system can detect more correct events with less false alarms. Third, a Markov

clips. In the second one, we use spatio-temporal features extracted from each video clip to learn a dictionary of spatio-temporal words and use a bag-of-features (BoF) approach to classify new video clips. In the third approach, we use multiple kernel learning to combine the LDS and BoF approaches. Our

First and foremost, I would like to thank my advisor, Prof. Jennifer E. Michaels. I am most appreciative of your guidance over the last four years, from the basics of elastic waves to both short- and long-term career advice. I have grown a tremendous amount, both profes-sionally and personally