PPT-On Multiple Kernel Learning

Author : lindy-dunigan | Published Date : 2016-05-20

with Multiple Labels Lei Tang Jianhui Chen and Jieping Ye Kernelbased Methods Kernelbased methods Support Vector Machine SVM Kernel Linear Discriminate Analysis

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On Multiple Kernel Learning: Transcript

with Multiple Labels Lei Tang Jianhui Chen and Jieping Ye Kernelbased Methods Kernelbased methods Support Vector Machine SVM Kernel Linear Discriminate Analysis KLDA Demonstrate success in various domains. 1 Hilbert Space and Kernel An inner product uv can be 1 a usual dot product uv 2 a kernel product uv vw where may have in64257nite dimensions However an inner product must satisfy the following conditions 1 Symmetry uv vu uv 8712 X 2 Bilinearity Micchelli CAM MATH ALBANY EDU Department of Mathematics and Statistics State University of New York The University at Albany 1400 Washington Avenue Albany NY 12222 USA Massimiliano Pontil PONTIL CS UCL AC UK Department of Computer Science University 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 . 13:. . Alpaydin. :. . Kernel Machines. Coverage in Spring 2011: Transparencies for which it does not say . “cover. ” . will be skipped!. COSC 6342: Support Vectors . and using SVMs/Kernels for Regression, . 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. 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 Perceptrons. The . perceptron. A. B. instance. . x. i. Compute: . y. i. = . sign(. v. k. . . . x. i. . ). ^. y. i. ^. y. i. If mistake: . v. k+1. = . v. k. + . y. i. . x. i. . x . is a vector. 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. J. Saketha Nath. , IIT Bombay. Collaborators:. Pratik . Jawanpuria. , . Arun. . Iyer. , Sunita . Sarawagi. , Ganesh Ramakrishnan.. Outline. Introduction to Representation Learning. Summary of Research. 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. Vapnik. Good empirical results. Non-trivial implementation. Can be slow and memory intensive. Binary classifier. Was the big wave before graphical models and then deep learning, important part of your knowledge base. Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . Questions on . edX. Edge. Elliott Smith. Emerging Technologies Librarian. Koshland Bioscience Library. Waves of Innovation May 2016. “. Testing is a powerful means of . improving learning. , not just assessing .

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