PPT-Learning Classifiers from Distributional Data

Harris T Lin Sanghack Lee Ngot Bui and Vasant Honavar Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University htliniastateedu Introduction. se 1 Introduction Distributional approaches to meaning acquisition utilize distributional proper ties of linguistic entities as the building blocks of semantics In doing so they rely fundamentally on a set of assumptions about the nature of language 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. Ata . Kaban. Motivation & beginnings. Suppose we have a learning algorithm that is guaranteed with high probability to be slightly better than random guessing – we call this a . weak learner. E.g. if an email contains the work “money” then classify it as spam, otherwise as non-spam. Katrin Erk. University of Texas at . Austin. Meaning in Context Symposium. München. September 2015. Joint work with Gemma . Boleda. Semantic features by example: . Katz & Fodor. Different meanings of a word characterized by lists of semantic features. Which of the two options increases your chances of having a good grade on the exam? . Solving the test individually. Solving the test in groups. Why?. Ensemble Learning. Weak classifier A. Ensemble Learning. Chains of. Multiple Interlinked RDF Data Stores. Harris T. . Lin . and . Vasant. . Honavar. Artificial Intelligence Research Laboratory. Department of Computer Science. Iowa State University. htlin@iastate.edu. Lifeng. Yan. 1361158. 1. Ensemble of classifiers. Given a set . of . training . examples, . a learning algorithm outputs a . classifier which . is an hypothesis about the true . function f that generate label values y from input training samples x. Given . Katrin . Erk. You can get an idea of what a word means from observing it in context. He filled the . wampimuk. , passed it around, and we all drank some. We found a little hairy . wampimuk. . sleeping behind a tree. . . Nathalie Japkowicz. School of Electrical Engineering . & Computer Science. . University of Ottawa. nat@site.uottawa.ca. . Motivation: My story. A student and I designed a new algorithm for data that had been provided to us by the National Institute of Health (NIH).. . Nathalie Japkowicz. School of Electrical Engineering . & Computer Science. . University of Ottawa. nat@site.uottawa.ca. . Motivation: My story. A student and I designed a new algorithm for data that had been provided to us by the National Institute of Health (NIH).. Machine Learning Algorithms . Mohak . Shah Nathalie . Japkowicz. GE . Software University of Ottawa. ECML 2013, . Prague. “Evaluation is the key to making real progress in data mining”. [Witten & Frank, 2005], p. 143. 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. What is an IDS?. An . I. ntrusion . D. etection System is a wall of defense to confront the attacks of computer systems on the internet. . The main assumption of the IDS is that the behavior of intruders is different from legal users.. Ifeoma. Nwogu. i. on. @. cs.rit.edu. Lecture . 13 . – . Classifiers for images. Schedule. Last class . RANSAC and robust line fitting. Today. Review mid-term. Start classifiers. Readings for today: .