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 . This tutorial takes the first steps in building a strong foundation to understanding of the SAS macro facility Macro variables are introduced as parameters to a SAS program Then INCLUDE is added and the power of this combination demonstrated Macros 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 inten- intention: intention: intention: demanding liar mental planning reactions. liar a a H a a a a S S a S S phase, C a a content characteristics a a C C C S a a S d ELECTRON (Overview): 6 parts, 22 arms. Phase . 2. Treatment. Naïve and Treatment . Experienced. Source: . Gilead Sciences, . Inc. Sofosbuvir. Summary of ELECTRON Trials Design (1 of 2). Part 1 . - . Some Other Efficient Learning Methods. William W. Cohen. Two fast algorithms. Naïve Bayes: one pass. Rocchio. : two passes. if vocabulary fits in memory. Both method are algorithmically similar. count and combine. Theparadigmisoftheoreticalinterestbecauseitshowsthatthereisafun-damentalalternativetothedominantapproachtoclassicationlearning.Thedominantapproachperformssearchthroughahypothesisspacetoidentifythehyp Using . MapReduce. Jacopo . Urbani. , Spyros . Kotoulas. ,. Eyal. Oren, and Frank van . Harmelen. Department of Computer Science,. Vrije. . Universiteit. Amsterdam,. The Netherlands . . . Mark S. Sulkowski, MD. Medical Director, Viral Hepatitis Center. Divisions of Infectious Diseases and Gastroenterology/Hepatology. Johns Hopkins University School of Medicine. Baltimore, Maryland. Treatment-Naive Data . 2. Poisson compositing. 3. our result. 4. Objectives. Robust to inaccurate selection. Output Quality. - Limit color bleeding. Time-Performance. - . Efficient method. 5. Seamless compositing. Poisson Compositing Result . CD8+ Effector T Cells. DIFFERENTIATION OF CD8+ T CELLS INTO . CYTOTOXIC T . LYMPHOCYTES. Induction . and effector . phases of CD8+ . T cell . responses. Nature of Antigen and Antigen-Presenting . Cells for . Hadoop. ). . COSC 526 Class 3. Arvind Ramanathan. Computational Science & Engineering Division. Oak Ridge National Laboratory, Oak Ridge. Ph. : 865-576-7266. E-mail: . ramanathana@ornl.gov. . Hadoop. 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. . SPRINT-2. Phase 3, Treatment . Naive. Treatment. . Naïve. Source: . Poordad F, . et al. . N Engl J Med. 2011;364:1195-206.. Boceprevir . for Treatment-Naïve HCV Genotype 1. SPRINT. -2 Trial: Study Design. . 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.