PPT-Machine Learning – Classifiers and Boosting

Reading Ch 1861812 2012032 Not Ch 185 Outline Different types of learning problems Different types of learning algorithms Supervised learning Decision trees Naïve Bayes Perceptrons. Tom M Mitchell All rights reserved DRAFT OF January 19 2010 PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS PERMISSION This is a rough draft chapter intended for inclusion in a possible second edition of the textbook Machine Learn ing TM Mitchell McGraw H Rob J Hyndman OB H YNDMAN MONASH EDU Department of Econometrics and Business Statistics Monash University Clayton VIC 3800 Australia Abstract Multistep forecasts can be produced recursively by iterating a onestep model or directly using a speci64257 Machine: Adversarial Detection . of Malicious . Crowdsourcing Workers . Gang . Wang. , Tianyi Wang, Haitao . Zheng, Ben . Y. Zhao . UC Santa Barbara. gangw@cs.ucsb.edu. Machine Learning for Security. Ata . Kaban. Motivation & beginnings. Suppose we have a learning algorithm that is guaranteed with high probability to be slightly better than random guessing – we call this a . weak learner. E.g. if an email contains the work “money” then classify it as spam, otherwise as non-spam. learning and prediction. Jongmin. Kim. Seoul National University. Problem statement. Predicting outcome of surgery. Predicting outcome of surgery. Ideal approach. . . . .. ?. Training Data. Predicting outcome. CSE 5095: Special Topics Course. Boosting. Nhan Nguyen. Computer Science and Engineering Dept.. Boosting. Method for converting rules of thumb into a prediction rule.. Rule . of thumb. ?. Method?. Binary Classification. A “paradigmatic” method for real-time object detection . Training is slow, but detection is very fast. Key ideas. Integral images. for fast feature evaluation. Boosting. for feature selection. Attentional. Lifeng. Yan. 1361158. 1. Ensemble of classifiers. Given a set . of . training . examples, . a learning algorithm outputs a . classifier which . is an hypothesis about the true . function f that generate label values y from input training samples x. Given . . 1. Sai Koushik Haddunoori. Problem:. E-mail provides a perfect way to send . millions . of advertisements at no cost for the sender, and this unfortunate fact is nowadays extensively exploited by several . CMPUT 615. Boosting Idea. . We have a weak classifier, i.e., it’s error rate is a little bit better than 0.5.. . . Boosting combines a lot of such weak learners to make a strong classifier (the error rate of which is much less than 0.5). Admin. Final project. Ensemble learning. Basic idea: . if one classifier works well, why not use multiple classifiers!. Ensemble learning. Basic idea: . if one classifier works well, why not use multiple classifiers!. Boosting. Nhan Nguyen. Computer Science and Engineering Dept.. Boosting. Method for converting rules of thumb into a prediction rule.. Rule . of thumb. ?. Method?. Binary Classification. X: set of all possible instances or examples. . Florina. . Balcan. 03/18/2015. Perceptron, Margins, Kernels. Recap from last time: Boosting. Works by creating . a series . of challenge datasets . s.t.. . even modest performance on these can . be . What is an IDS?. An . I. ntrusion . D. etection System is a wall of defense to confront the attacks of computer systems on the internet. . The main assumption of the IDS is that the behavior of intruders is different from legal users..