PPT-Naïve

Bayes William W Cohen Probabilistic and Bayesian Analytics Andrew W Moore School of Computer Science Carnegie Mellon University wwwcscmueduawm awmcscmuedu 4122687599 Note to other teachers and users of these slides Andrew would be delighted if you found this source material useful in giving your own lectures Feel free to use these slides verbatim or to modify them to fit your own needs PowerPoint originals are available If you make use of a significant portion of these slides in your own lecture please include this message or the following link to the source repository of Andrews tutorials . John Braun University of Western Ontario Journal of Statistics Education Volume 20 Number 2 2012 httpwwwamstatorgpublicationsjsev20n2braunpdf Copyright 2012 by W John Braun all rights reserved This text may be freely shared among individuals but it Tom M Mitchell All rights reserved DRAFT OF January 19 2010 PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS PERMISSION This is a rough draft chapter intended for inclusion in a possible second edition of the textbook Machine Learn ing TM Mitchell McGraw H 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 This new algorithm is often faster than comparable raytracing methods at rendering dynamic scenes and has a similar level of performance when compared to static raytracers Memory management is made minimal and deterministic which simpli64257es raytr Don’t Be Naïve!. “Go, and make a careful search for the Christ child, So that I too may go and worship him.” . (Matthew 2:8). DON’T BE NAÏVE!. DON’T BE NAÏVE!. DON’T BE NAÏVE!. DON’T BE NAÏVE!. Bayes. William W. Cohen. Probabilistic and Bayesian Analytics. Andrew W. Moore. School of Computer Science. Carnegie Mellon University. www.cs.cmu.edu/~awm. awm@cs.cmu.edu. 412-268-7599. Note to other teachers and users of these slides. Andrew would be delighted if you found this source material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are available. If you make use of a significant portion of these slides in your own lecture, please include this message, or the following link to the source repository of Andrew’s tutorials: . Theparadigmisoftheoreticalinterestbecauseitshowsthatthereisafun-damentalalternativetothedominantapproachtoclassicationlearning.Thedominantapproachperformssearchthroughahypothesisspacetoidentifythehyp 2The naive view therefore provides a kind of indirect evidence for what I call intentionalism: theview that all mental states are intentional.3Intentionalism is a controversial doctrine, and conscious Naively, we would attempt batch proximal gradient descent on this objective function, which would involve the following steps: 1. Given current iterate θ , calculate current λ for al 2A naive solution, at least for security, is simply to issue encryption keys to clients, storing only encrypteddata at vulnerable repositories. A weakness of this approach is that it does not address bayes. ICCM - 2017. Using naïve . bayes. A classification algorithm. Naïve Bayes is popular due to its simplicity of implementation and overall effectiveness. Based on (of course) Bayes theorem. “Naïve” because of no dependency between words. . Case. 47-Year-Old . Man With Asymptomatic HIV Infection. Case (cont). Initial Clinical Presentation. Laboratory Results. HepaScore. ®. A Composite Biomarker Panel for Liver Fibrosis. Hepatic Steatosis in Patients With HIV/HCV Coinfection. 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 . WEKA üzerinde . Uygulaması. Ahmet . Cevahir ÇINAR. Naive. . Bayes. sınıflandırma algoritması, . adını . Matematikçi . Thomas . Bayes. ’den. . alan . bir . sınıflandırma algoritmasıdır.