PPT-A Fast Kernel for

Attributed Graphs Yu Su University of California at Santa Barbara with Fangqiu Han Richard E Harang and Xifeng Yan Introduction A Fast Kernel for Attributed Graphs Graph Kernel. IK. November 2014. Instrument Kernel. 2. The Instrument Kernel serves as a repository for instrument specific information that may be useful within the SPICE context.. Always included:. Specifications for an instrument’s field-of-view (FOV) size, shape, and orientation. Debugging as Engineering. Much of your time in this course will be spent debugging. In industry, 50% of software dev is debugging. Even more for kernel development. How do you reduce time spent debugging?. Osck. Owen Hofmann, Alan Dunn, . Sangman. Kim, . Indrajit Roy*, Emmett Witchel. UT Austin. *HP Labs. Rootkits are dangerous. Adversary exploits insecure system. Leave backdoor . to facilitate long-term access. Theodore . Trafalis. (joint work with R. Pant). Workshop on Clustering and Search Techniques in Large Scale . Networks, LATNA. , Nizhny Novgorod, Russia, November 4, 2014. Research questions. How can we handle data uncertainty in support vector classification problems?. Steven C.H. Hoi, . Rong. Jin, . Peilin. Zhao, . Tianbao. Yang. Machine Learning (2013). Presented by Audrey Cheong. Electrical & Computer Engineering. MATH 6397: Data Mining. Background - Online. with Multiple Labels. Lei Tang. , . Jianhui. Chen and . Jieping. Ye. Kernel-based Methods. Kernel-based methods . Support Vector Machine (SVM). Kernel Linear Discriminate Analysis (KLDA). Demonstrate success in various domains. Rootkits. with lightweight Hook Protection. Authors: . Zhi. Wang, . Xuxian. Jiang, . Weidong. Cui, . Peng. . Ning. Presented by: . Purva. . Gawde. Outline. Introduction. Prior research. Problem overview. Presented by:. Nacer Khalil. Table of content. Introduction. Definition of robustness. Robust Kernel Density Estimation. Nonparametric . Contamination . Models. Scaled project Kernel Density Estimator. method . introduction. hyperplane. Margin. W. . =. . 0.  .  . . =. . -1.  . separating hyperplane. support hyperplane. support hyperplane. hyperplane. /||w||=1/||w||.  . /||w||=1/||w||.  . Margin. Machine Learning. March 25, 2010. Last Time. Basics of the Support Vector Machines. Review: Max . Margin. How can we pick which is best?. Maximize the size of the margin.. 3. Are these really . “equally valid”?. Jose C. . Principe. Computational . NeuroEngineering. . Laboratory (CNEL). University . of Florida. principe@cnel.ufl.edu. Acknowledgments. Dr. Weifeng Liu, Amazon. Dr. . Badong. Chen, . Tsinghua. University and Post Doc CNEL. David Ferry, Chris Gill. Department of Computer Science and Engineering. Washington University, St. Louis MO. davidferry@wustl.edu. 1. Traditional View of Process Execution. However, the kernel is not a traditional process!. CSE . 422S . - . Operating . Systems Organization. Washington University in St. Louis. St. Louis, MO 63143. 1. Things Happen: Kernel Oops vs Panic. A kernel . panic. is unrecoverable and results in an instant halt. Multipole. . Methods. Qi Hu, Nail A. Gumerov, Ramani Duraiswami. Institute for Advanced Computer Studies . Department of Computer Science. University of Maryland, College Park, MD. Previous work. FMM .