PPT-Naive Bayesian Classification

Abel Sanchez John R Williams Stunningly Simple The mathematics of Bayes Theorem are stunningly simple In its most basic form it is just an equation with three known variables and one unknown one . ca Abstract Naive Bayes is one of the most ef64257cient and effective inductive learning algorithms for machine learning and data mining Its competitive performance in classi64257ca tion is surprising because the conditional independence assumption o De64257nition A Bayesian nonparametric model is a Bayesian model on an in64257nitedimensional parameter space The parameter space is typically chosen as the set of all possi ble solutions for a given learning problem For example in a regression prob P(. A . &. B. ) . = . P(. A. |. B. ) * P(. B. ). Product Rule:. Bayesian Reasoning. P(. A . &. B. ) . = . P(. A. |. B. ) * P(. B. ). Product Rule:. Shorthand for . . P(A=true & B=true) = P(A=true | B=true) * P(B=true). Department of Electrical and Computer Engineering. Zhu Han. Department. of Electrical and Computer Engineering. University of Houston.. Thanks to Nam Nguyen. , . Guanbo. . Zheng. , and Dr. . Rong. . Week 9 and Week 10. 1. Announcement. Midterm II. 4/15. Scope. Data . warehousing and data cube. Neural . network. Open book. Project progress report. 4/22. 2. Team Homework Assignment #11. Read pp. 311 – 314.. Misstear. Spam Filtering. An Artificial Intelligence Showcase. What is Spam. Messages sent indiscriminately to a large number of recipients. We all hate it. Term attributed to a Monty Python skit. Legitimate messages sometimes referred to as “ham. Alex Yakubovich. Elderlab. Oct 7, 2011. John Wilder, Jacob Feldman, Manish Singh, . Superordinate shape classification using natural shape statistics. , Cognition, Volume 119, Issue 3, June 2011, Pages 325-340. http://xkcd.com/1236/. Bayes. Rule. The product rule gives us two ways to factor . a joint probability:. Therefore,. Why is this useful?. Can update our beliefs about A based on evidence B. . P(A) is the . Making Decisions Under uncertainty. 1. Overview. Basics of Probability and the Bayes Rule. Bayesian . Classification. Losses and . Risks. Discriminant Function. Utility Theory. Association . Rule Learning. http://xkcd.com/1236/. Bayes. Rule. The product rule gives us two ways to factor . a joint probability:. Therefore,. Why is this useful?. Can update our beliefs about A based on evidence B. . P(A) is the . Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. August 14, 2014. Bayes’ Theorem. Thomas Bayes (1701-1761). Simple form of Bayes’ Theorem, for two random variables . Sefik Emre Eskimez, Kenneth Imade, Na Yang, Melissa Sturge-Apple, Zhiyao Duan, Wendi . Heinzelman. University of . Rochester. Motivation. Emotions play . a vital . role in social . interactions. Realistic human-computer interactions require . Sjors . H.W. Scheres. EMBO course . 2019. Birkbeck. College, London. Agenda. An intuitive introduction. Alignment. Dealing with the incomplete problem. maxCC. . vs. ML (real-space). Classification. Jingjing Ye, PhD. BeiGene. PSI Journal Club: Bayesian Methods. Nov. 17, 2020. Outline. Background . Using a case study to illustrate potential useful Bayesian analysis. Analysis and monitoring. Design study.