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. 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. Author: Yang Song et al. (Google). Presenters:. Phuc Bui & Rahul . Dhamecha. 1. Introduction. Taxonomic classification . for . web-based videos. Web-based Video Classification. Web-based . Video (e.g. . 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 . Towards Bridging Semantic Gap and Intention Gap in Image Retrieval. Hanwang. Zhang. 1. , . Zheng. -Jun Zha. 2. , Yang Yang. 1. , . Shuicheng. Yan. 1. , . Yue. Gao. 1. , Tat-. Seng. Chua. 1. 1: National University of Singapore. . Nathalie Japkowicz. School of Electrical Engineering . & Computer Science. . University of Ottawa. nat@site.uottawa.ca. . Motivation: My story. A student and I designed a new algorithm for data that had been provided to us by the National Institute of Health (NIH).. Tactile Classifiers and Maps. Chapter 4.3.2. Overview. Tactile ASL is emerging as a variety of ASL that is used by fluent ASL signers who are blind. . This presentation describes the technique of signing on the listener’s arms and/or hand in order to make spatial relationships more clear.. Tonight, . you will learn. …. Introductions to ASL classifiers.  . About classifiers that show the . size and shape of an object. .  . About classifiers that indicate how an object is moved or placed. . Machine Learning Algorithms . Mohak . Shah Nathalie . Japkowicz. GE . Software University of Ottawa. ECML 2013, . Prague. “Evaluation is the key to making real progress in data mining”. [Witten & Frank, 2005], p. 143. 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 . Given: Set S {(x)} xX, with labels Y = {1, Want to keep your testosterone levels healthy? Eating these 5 best testosterone boosting foods that are high in nutrients & vitamins.