PPT-Lab #10: Demonstration of AdaBoost

1 2 Our Data Chest Pain Blocked Arteries Patient Weight Heart Disease Yes Yes 205 Yes No Yes 180 Yes Yes No 210 Yes Yes Yes 167 Yes No Yes 156 No No Yes 125 No Yes No. The current demonstration is capped at 20000 individua ls with a maximum DSH diversion of 90 million total computable AMENDMENTS Multiple amendments were submitted and approved at the same time Amendments were submitted on April 2 2004 August 2 2004 Recommended to Arkansans by Easter H Tucker. Associate Professor – Family and Consumer Sciences Specialist. Originally developed by Barbara Brown, Ph.D., R.D./L.D.. Food Specialist Oklahoma Cooperative Extension Service. Usman Roshan. Bagging. Randomly sample training data. Determine classifier . C. i. on sampled data. Goto. step 1 and repeat . m. times. For final classifier output the majority . vote. Popular example: random forest. AdaBoost. Indraneel. . Mukherjee. Cynthia Rudin. Rob . Schapire. AdaBoost. (Freund and . Schapire. 97). AdaBoost. (Freund and . Schapire. 97). Basic properties of . AdaBoost’s. convergence are still not fully understood.. Sonia Chernova Manuela . Veloso. Carnegie Mellon University. Computer Science Department. Policy Development. Learning from Demonstration:. Access/select sensor data. Develop actions. Provide demonstrations. MONEY FOLLOWS . THE PERSON (MFP). DEMONSTRATION GRANT. Demonstration Services. Workshop. Montana will create a sustainable system that supports community options as a first choice for individuals needing long term care services.. 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 . Ms. Bermudez . Demonstration Speech .  . In a nutshell, a good demonstration speech . teaches. . It's a variation of the informative speech with in-built visual aids. The audience, through listening, watching or participating, learns something new.. Admin. Final project. Ensemble learning. Basic idea: . if one classifier works well, why not use multiple classifiers!. Ensemble learning. Basic idea: . if one classifier works well, why not use multiple classifiers!. -Fujishin on page 136. Demonstration Speeches . Provides answers to the question “How do you do that. ?” (Showing a skill set). Think ‘VERB/NOUN’ (baking cookies, shooting basketball, cleaning gun, avoiding tickets, etc…). IMPORTANT - GO GREEN Please Retain All Packing Materials Opening the accompanying shipment constitutes agreement with the following terms and conditions. Terms and Conditions : The following details t APPROACHtheimplementedProtectOhioWaiverDemonstrationparentstheirrequiredplacementapproachenhancetheinteractionthelikelihoodandfrequencydocumentationimportantaspectsneedsgivenactivitiesparentschildrend Page 1of 69Approval Period August 1 2017 through June 30 2022AmendedJune 8 2018MANAGED MEDICAL ASSISTANCESECTION 1115 DEMONSTRATIONWAIVER AUTHORITIESNUMBER11-W-00206/4TITLEManagedMedical Assistance Pr 2010 (and beyond). Plans & Milestones. 2010 – A Year of Transition. Phase 1 ends – Phase 2 begins. This is our last meeting in Phase 1. Seamless transition. No major re-structuring etc.. Adiabatic “shift in emphasis” needed.