PPT-Medical Data Classifier

Author : pamella-moone | Published Date : 2016-09-16

undergraduate project By Avikam Agur and Maayan Zehavi Advisors Prof Michael Elhadad and Mr Tal Baumel Motivation word2vec An algorithm that associates closelyrelated

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Medical Data Classifier: Transcript

undergraduate project By Avikam Agur and Maayan Zehavi Advisors Prof Michael Elhadad and Mr Tal Baumel Motivation word2vec An algorithm that associates closelyrelated words Combin ing with the outcome of our project this algorithm will help creating a medical text summarizer. Bagging and Boosting. Cross-Validation. ML and Bayesian Model Comparison. Combining Classifiers. Resources:. MN: Bagging and Decision Trees. DO: Boosting. WIKI: . AdaBoost. AM: Cross-Validation. CV: Bayesian Model Averaging. Dominic . Cockman. , . Jesper. Madsen, . Qiuzhen. Zhu. 1. L. earning . C. lassifier. . S. ystems. History and Motivations. 2. History and Motivations for LCS. Robust . machine learning techniques that can be applied to classification tasks, large-scale data mining problems or robot control and cognitive system applications, among . Reading. Ch. 18.6-18.12, 20.1-20.3.2. (Not Ch. 18.5). Outline. Different types of learning problems. Different types of learning algorithms. Supervised learning. Decision trees. Naïve Bayes. Perceptrons. . Using SVM Classification to . Identify . Rhetorical . Moves in the Enron Email Corpus. Ryan M. Omizo. @. OmizoRM. WIDE-MATRIX Research. Ryan M. Omizo. @. OmizoRM. Supervised Learning. Machine learning algorithm fits a classifier to data coded by humans according to given classes. Ensemble Methods. Bamshad Mobasher. DePaul University. Ensemble methods. Use a combination of models to increase accuracy. Combine a series of k learned models, . M. 1, . M. 2, …, . Mk. , with the aim of creating an improved model . Project 2. Final for CS240A: Project . II:. Data . Mining in SQL and . Datalog. .. In this project, you will gain experience and understanding on the problem that DB query languages are facing in supporting Predictive Analytics even when the task is as simple as . 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. Lucy . Kuncheva. School of Computer Science. Bangor University. mas00a@bangor.ac.uk. . Part 1. 1. What is Pattern Recognition? . Data set: objects, features, class labels. Classifiers and classifier ensembles. 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!. & . Machine Learning. George Nagy. Professor Emeritus, RPI. I am obliged for this material to current and former colleagues . and students, and the web. Only the mistakes are strictly my own. .. BHSAI. Jinbo. Bi, . Ph.D.. HR. SBP. SpO2. MAP. DBP. RR. 0. 2. 4. 6. 8. 10. 12. 14. 16. Time (min). HR. RR. SBP. SpO2. MAP. DBP. 60. 100. 140. 80. 100. 40. 120. 200. 20. 40. 60. 80. mmHg. . % . bpm. Virginia Polytechnic Institute and State University. Blacksburg, Virginia 24061. Professor: E. Fox. Presenters:. Saurabh Chakravarty,. Eric Williamson. December 1, 2016. Table of contents. Problem Definition. and . Hsinchun. . Chen. Spring . 2016. , MIS . 496A. Acknowledgements:. Mark Grimes, Gavin Zhang – University of Arizona. Ian H. Witten – University of Waikato. Gary Weiss – Fordham University . Given: Set S {(x)} xX, with labels Y = {1,

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