PPT-SVM QP & Midterm Review

Rob Hall 10142010 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. Bayes rule. Popular classification methods. Logistic regression . Linear discriminant analysis (LDA)/QDA and Fisher criteria. K-nearest neighbor (KNN). Classification and regression tree (CART). Bagging. Chen-Tse Tsai and . Siddharth. Gupta. Outline. Introduction to SVM . Large . Margin Methods for Structured and Interdependent Output Variables (Tsochantaridis et. al., 2005). Max-Margin Markov Networks (Taskar et. . Network. . Ben . Taskar. ,. . Carlos . Guestrin. Daphne . Koller. 2004. Topics Covered. Main Idea.. Problem Setting.. Structure in classification problems.. Markov Model.. SVM. Combining SVM and Markov Network.. Active Learning for. Hyperspectral Image Classification. School of Civil Engineering, Purdue University. and. Laboratory for Applications of Remote Sensing. Email: . {wdi@purdue.edu. 1. , . mcrawford. Warm Up. What did you think of the test yesterday?. What topic(s) do you think you need the most practice/review of before the midterm?. Solve the following equation for y: ax + (by). 2. = c. Write down everything you can think of when you hear “piecewise function.”. David Kauchak. CS 451 – Fall 2013. Admin. Assignment 5. Midterm. Friday’s class will be in MBH 632. CS lunch talk Thursday. Java tips for the data. -. Xmx. -Xmx2g. http://. www.youtube.com. /. watch?v. Tomer. . Meshorer. Agenda. This presentation describes the use of speech recognition for:. . HCI . for spastic . dysarthria. patients . [M. Hasegawa-Johnson]. Identify progression of . P. arkinson disease using speech signal[A. 15-213: Introduction to Computer Systems. Recitation 8: Monday. , Oct. 19, 2015. Ben Spinelli. Agenda. Midterm Logistics. Brief Overview of . some. topics. Practice Questions. Midterm. Tues Oct 20. th. 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 . Ethan Grefe. December . 13, . 2013. Motivation. Spam email . is constantly cluttering inboxes. Commonly removed using rule based filters. Spam often has . very similar characteristics . This allows . Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. Learning for NLP Midterm Review: Midterm next Tuesday Homework back Thanks for doing midterm exam! Some very useful comments came in. Today Statistical NLP Machine Learning for NL Tasks Some form of classification Midterm Review What you need to know for your Midterm: American Literary History through 1914 Terminology used in discussing literature How to find rhyme, rhythm and meter How to do basic literary analysis Practice Problem. Workshop 2. Practice Problem Midterm Review Fall 2019 / E. . Djoewanda. Solutions Q1. Practice Problem Midterm Review Fall 2019 / E. . Djoewanda. R – E = NI. CS + R.E = Total S.E.