PPT-BOOSTING & ADABOOST

Lecturer Yishay Mansour Itay Dangoor Overview Introduction to weak classifiers Boosting the confidence Equivalence of weak amp strong learning Boosting the accuracy recursive construction . Usman Roshan. Bagging. Randomly sample training data. Determine classifier . C. i. on sampled data. Goto. step 1 and repeat . m. times. For final classifier output the majority . vote. Popular example: random forest. 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. 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. By . Yoav. Freund . and Robert E. . Schapire. Presented by David Leach. Original . Slides by Glenn . Rachlin. 1. Outline:. Background. On-line allocation of resources . Introduction . The Problem. The Hedge Algorithm . 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 . Decision Stumps. Let . x. = (. x. 1. , . x. 2. , . …, . x. n. ). Decision . Stump . h. i,t. . Training Decision Stumps. Given data of the form . x. = (. x. 1. , . x. 2. , . …, . x. n. ), . one run of the training . 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). David . Mease. & . Abraham . Wyner. What is the Statistical View? . The idea presented in . J. . Friedman, T. Hastie, and R. . Tibshirani. . Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28:337–374, . Boost Living is a strong community of professional gamers and they all have been in the gaming market for more than 5 years. When they started they only have a small number of people associated with the community who just did Pandarian Challenge mode boost. 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 . CoinLooting is a successful German company that specializes in gaming services and have a lot of experience in the field of gold and boosting services of all kinds. Therefore, CoinLooting offers you a swift and premium-quality service – at the best price attainable. Visit: https://www.coinlooting.com/ 1. 2. Our Data. Chest Pain. Blocked Arteries. Patient Weight. Heart Disease. Yes. Yes. 205. Yes. No. Yes. 180. Yes. Yes. No. 210. Yes. Yes. Yes. 167. Yes. No. Yes. 156. No. No. Yes. 125. No. Yes. No.