PPT-Support Vector Machines and Kernel Methods

Machine Learning March 25 2010 Last Time Recap of the Support Vector Machines Kernel Methods Points that are not linearly separable in 2 dimension might be linearly separable in 3 Kernel 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. Reading: . Ben-. Hur. and Weston, “A User’s Guide to Support Vector Machines” . (linked from class web page). Notation. Assume a binary classification . problem: . h. (. x. ) .  {−1, 1}. 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.. 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”?. 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. SVM criterion: maximize the . margin. , or distance between the hyperplane and the closest training example. Support vector machines. When the data is linearly separable, which of the many possible solutions should we prefer?. Chapter 09. Disclaimer: . This PPT is modified based on . IOM 530: Intro. to Statistical Learning. STT592-002: Intro. to Statistical Learning . 1. 9.1 . Support Vector Classifier. Applied Modern Statistical Learning Methods. 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 . Chen. Support . Vector Machines. The Basic Method. Support vector machines are a type of supervised binary linear . classifier. The idea behind support vector machines is to draw a hyperplane between two linearly separable groups of . Retrieval . Evaluation. Thorsten Joachims. , . Madhu. Kurup, Filip Radlinski. Department of Computer Science. Department of Information Science. Cornell University. Decide between two Ranking Functions. 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. 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.