PPT-Boosting of classifiers

Ata Kaban Motivation amp 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 Eg if an email contains the work money then classify it as spam otherwise as nonspam. Reading. Ch. 18.6-18.12, 20.1-20.3.2. (Not Ch. 18.5). Outline. Different types of learning problems. Different types of learning algorithms. Supervised learning. Decision trees. Naïve Bayes. Perceptrons. Handshapes that represent people, objects, and descriptions.. Note: You cannot use the classifier without naming the object first.. Types of Classifiers. We will look at the types of classifiers . Size and Shape . CS311, Spring 2013. Linear Classifiers/SVMs. Admin. Midterm exam posted. Assignment 4 due Friday by 6pm. No office hours tomorrow. Math. Machine learning often involves a lot of math. some aspects of AI also involve some familiarity. 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. Usman Roshan. CS 675. Comparison of classifiers. Empirical comparison of supervised classifiers – ICML 2006. Do we need hundreds of classifiers – JMLR 2014. Empirical comparison of supervised classifiers – ICML 2006 . Image . Denoising. Algorithms. The research leading to these results has received funding from the European Research Council under European Union's Seventh Framework . Program, . ERC Grant agreement no. . 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 . 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). 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/