PPT-Support Vector Machines and Kernel Methods

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. 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. 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.. (and Kernel Methods in general). Machine . Learning. 1. Last Time. Multilayer . Perceptron. /Logistic Regression Networks. Neural Networks. Error . Backpropagation. 2. Today. Support Vector Machines. A B M Shawkat Ali. 1. 2. Data Mining. ¤. . DM or KDD (Knowledge Discovery in Databases). Extracting previously unknown, valid, and actionable information . . . crucial decisions. ¤. . Approach. nearest neighbor. Probabilistic models:. Naive Bayes. Logistic Regression. Linear models:. Perceptron. SVM. Decision models:. Decision Trees. Boosted Decision Trees. Random Forest. Outline: . a toolbox of useful algorithms concepts. Why do we use machines?. Machines make doing work easier.. But they do not decrease the work that you do.. Instead, they . change the way you do work.. In general you trade more force for less distance or less force for more distance. 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 . 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?. 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 . 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. 3/6/15. Multiple linear regression. What are you predicting?. Data type. Continuous. Dimensionality. 1. What are you predicting it from?. Data type. Continuous. Dimensionality. p. How many data points do you have?. Asa. . Ben-. Hur. , . David . Horn, . Hava. T. . Siegelmann. , . Vladimir Vapnik. Zhuo Liu. Clustering. G. rouping . a set of objects . which . are . similar. Similarity: distance, density, statistical distribution.