PPT-Online Kernel Learning

Jose C Principe Computational NeuroEngineering Laboratory CNEL University of Florida principecnelufledu Acknowledgments Dr Weifeng Liu Amazon Dr Badong Chen Tsinghua University and Post Doc CNEL. 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 By George Kour. Supervised by Dr. Raid . Saabne. Machine Learning (Optional). Main model (PAC). Pattern Recognition(Optional). Supervised learning vs. unsupervised learning. Classification techniques. The Point/Counterpoint Examination. Kaitlyn. Hagan. Breanna. . Byington. Charles Emory. Matt Kautz. Any online learners?. What is online learning?. Pure online learning. Internet-based education outside the bounds of a normal classroom without a physical teacher present to assist in completion of online assignments.. 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. 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 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. J. Saketha Nath. , IIT Bombay. Collaborators:. Pratik . Jawanpuria. , . Arun. . Iyer. , Sunita . Sarawagi. , Ganesh Ramakrishnan.. Outline. Introduction to Representation Learning. Summary of Research. Introduction and Questions. EDC&I 505. Spring, 2012. Agenda for This Evening. A few preliminary questions. Introductions. You. Me. The Course. An introductory presentation. History, recent background, directions now. Janine Lim, PhD. Associate Dean, Online Higher Education. Andrews University. j. anine@andrews.edu. b. log.janinelim.com. Let none waste time in deploring . the . scantiness of their visible . resources. 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 . October 2011INTERNATIONAL ASSOCIATION FOR K-12 ONLINEEARNING The mission of the International Association for K-12 Online Learning iNACOL is to ensure all students have access to a world-class educati