PPT-Support Vector Machines (SVM)

Author : luanne-stotts | Published Date : 2018-10-13

Chapter 09 Disclaimer This PPT is modified based on IOM 530 Intro to Statistical Learning STT592002 Intro to Statistical Learning 1 91 Support Vector Classifier

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Support Vector Machines (SVM): Transcript

Chapter 09 Disclaimer This PPT is modified based on IOM 530 Intro to Statistical Learning STT592002 Intro to Statistical Learning 1 91 Support Vector Classifier Applied Modern Statistical Learning Methods. 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. Rob Hall . 10/14/2010. 1. This Recitation. Review of Lagrange multipliers (basic undergrad calculus). Getting to the dual for a QP. Constrained norm minimization (for SVM). Midterm review. 2. Minimizing a quadratic. Week 10 . Presented by Christina Peterson. Movement Exemplar-SVMs . Tran and . Torresani. [1] based the MEX-SVM on the work of . Malisiewicz. . et. al. . [2]. Linear SVMs applied to histograms of space-time interest points (STIPs) calculated from . (and Kernel Methods in general). Machine . Learning. 1. Last Time. Multilayer . Perceptron. /Logistic Regression Networks. Neural Networks. Error . Backpropagation. 2. Today. Support Vector Machines. Reading: . Ben-. Hur. & Weston, “A User’s Guide to Support Vector Machines”. . (linked from class web page). Notation. Assume a binary classification problem.. Instances are represented by vector . including Finite State Machines.. Finite State MACHINES. Also known as Finite State Automata. Also known as FSM, or State . Machines. Facts about FSM, in general terms. Finite State Machines are important . Rahul Sharma. Joint work with . Aditya. . Nori. (MSR India) and Alex Aiken (Stanford). Interpolants. If . then an . interpolant. . satisfies:. . contains only the variables common to . and . An . 2014: Anders Melen. 2015: Rachel Temple. The Nature of Statistical Learning Theory by V. Vapnik. 1. Table of Contents. Empirical Data Modeling. What is Statistical Learning Theory. Model of Supervised Learning. INTRODUCTION. An approach for classification that was developed in the computer science community in the 1990s.. Generalization of a classifier called the Maximal Margin Classifier.. HYPERPLANE. In a . What do they Try to Solve?. Hyperplanes. Property of the . Hyperplane. Separating . Hyperplane. The Maximal Margin . Hyperplane. . is the . Solution . to the . Optimization Problem. : . Maximal Margin Classifier. Machine learning:. Learn a Function from Examples. Function:. . Examples:. Supervised: . . Unsupervised: . . Semisuprvised. : . Machine learning:. Learn a Function from Examples. Function:. . Catherine Nansalo and Garrett Bingham. 1. Outline. Introduction to the Data. FG-NET database. Support Vector Machines. Overview. Kernels and other parameters. Results. Classifying Gender. Predicting Age. #The 16th China Beijing International Construction Machinery, Building Material Machines and Mining Machines Exhibition & Seminar#

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