PPT-Online Multiple Kernel Classification

Steven CH Hoi Rong Jin Peilin Zhao Tianbao Yang Machine Learning 2013 Presented by Audrey Cheong Electrical amp Computer Engineering MATH 6397 Data Mining Background Online. By George Kour. Supervised by Dr. Raid . Saabne. Machine Learning (Optional). Main model (PAC). Pattern Recognition(Optional). Supervised learning vs. unsupervised learning. Classification techniques. Classification Outline. Introduction, Overview. Classification using Graphs. Graph classification – Direct Product Kernel. Predictive Toxicology example dataset. Vertex classification – . Laplacian. Motivation. Operating systems (and application programs) often need to be able to handle multiple things happening at the same time. Process execution, interrupts, background tasks, system maintenance . Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?. Classifiers. Learn a decision rule assigning bag-of-features representations of images to different classes. 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?. PRESENTED BY . MUTHAPPA. Introduction. Support Vector Machines(SVMs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.. 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. 0.2 0.4 0.6 0.8 1.0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 kernel(b) kernel(c) kernel(d) (a)blurredimage(b)no-blurredimage0.900.981.001.021.10 (5.35,3.37)(4.80,3.19)(4.71,3.22)(4.93,3.23)(5.03,3.22 Pipeline . Parallelism from . Multiple . Dependent Kernels for . GPUs. Gwangsun Kim, . Jiyun Jeong, John Kim. Mark Stephenson. GPU Background. CTA. Kernel grid. CTA (Cooperative Thread Array). or Thread block. 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. Janghaeng Lee. , . Mehrzad. . Samadi. , and Scott . Mahlke. October, 2015. University . of Michigan - Ann . Arbor. University of Michigan. Electrical Engineering . and . Computer Science. Financial Modeling. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Introduction, Overview. Classification using Graphs. Graph classification – Direct Product Kernel. Predictive Toxicology example dataset. Vertex classification – . Laplacian. Kernel. WEBKB example dataset. Debmalya Panigrahi. Online Algorithms with Multiple Advice. Sreenivas Gollapudi, . Google. Amit Kumar, . IIT Delhi. Rong Ge, . Duke University. Keerti Anand, Duke University. MY PARTNERS-IN-CRIME. Online Algorithms with .