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. 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. 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. . 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 . Ludmila. . Kuncheva. School of Computer Science. Bangor University. mas00a@bangor.ac.uk. . Part 2. 1. Combiner. Features. Classifier 2. Classifier 1. Classifier L. …. Data set. A . . Combination level. . 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.. Tushar. . Khot. Joint work with . Sriraam. . Natarajan. , . Kristian. . Kersting. and . Jude . Shavlik. Sneak Peek. Present a method to learn structure and parameter for MLNs . simultaneously. Use functional gradients to learn many . 李秉昱. . Byeong-uk Yi. University of Toronto. b.yi@utoronto.ca. Kyungpook. National University. June 8, 2012. 1. Contents. The White Horse Paradox. Semantics of the White Horse Paradox. Classifiers & the Mass Noun Thesis. 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 . Sahil Patel. 1. , Justin Guo. 2. , Maximilian Wang. 2. Advisors: Dr. . Cuixian. (Tracy) Chen, Ms. Jessica Gray, Ms. Georgia Smith, Ms. Bailey Hall, Mr. Michael Suggs. 1. John T. Hoggard High School, . Want to keep your testosterone levels healthy? Eating these 5 best testosterone boosting foods that are high in nutrients & vitamins.