PPT-Statistical Learning Theory & Classifications Based on Support Vector Machines

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. Adam C Ioannidis NTUA School of Rural Surveying Engin eering Athens 15780 Greece katerinasardhotmailcom cioannidsurveyntuagr Commissio n ICWG IVa KEY WORDS Detection Classification Colour Automation Vision Learning ABSTRACT This paper examines t 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. (and Kernel Methods in general). Machine . Learning. 1. Last Time. Multilayer . Perceptron. /Logistic Regression Networks. Neural Networks. Error . Backpropagation. 2. Today. Support Vector Machines. Classifications . Based On. Support Vector Machines. 201. 6-05-02 presenter:. . Xipei Liu. Vapnik. , Vladimir. The nature of statistical learning theory. Springer Science & Business Media, 1995.. 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”?. 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. The Benefits of Reading Books,Most people read to read and the benefits of reading are surplus. But what are the benefits of reading. Keep reading to find out how reading will help you and may even add years to your life!.The Benefits of Reading Books,What are the benefits of reading you ask? Down below we have listed some of the most common benefits and ones that you will definitely enjoy along with the new adventures provided by the novel you choose to read.,Exercise the Brain by Reading .When you read, your brain gets a workout. You have to remember the various characters, settings, plots and retain that information throughout the book. Your brain is doing a lot of work and you don’t even realize it. Which makes it the perfect exercise!