PPT-Learning Classifier Systems

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 largescale data mining problems or robot control and cognitive system applications among . CSE 576. Face detection. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . 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. 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. Handshapes that represent people, objects, and descriptions.. Note: You cannot use the classifier without naming the object first.. Types of Classifiers. We will look at the types of classifiers . Size and Shape . Adaptation. in brain-computer interfaces. Introduction. Inherent . nonstationarity. of EEG. Why do we need ‘adaptation’ ?. varies between BCI sessions and within individual sessions. . due to a number of factors : changes in background brain activity, . Yulin . Shen. ECE 539 Presentation. 2013 Fall. Mushroom is a kind of food with high nutrition, however, it is sometimes poisonous!. A classification problem.. Develop some models for prediction.. . Dataset is from UCI Machine Learning . Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Are we still talking about diversity in classifier ensembles?. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Publications (580). Citations (4594). “CLASSIFIER ENSEMBLE DIVERSITY”. Search on 10 Sep 2014. MULTIPLE CLASSIFIER SYSTEMS 30. undergraduate project. By: Avikam Agur and Maayan Zehavi. Advisors: Prof. Michael Elhadad and Mr. Tal Baumel. Motivation. word2vec. : . An algorithm that associates closely-related words.. Combin. ing with the outcome of our project, this algorithm will help creating a medical text summarizer.. 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 . . 1. Sai Koushik Haddunoori. Problem:. E-mail provides a perfect way to send . millions . of advertisements at no cost for the sender, and this unfortunate fact is nowadays extensively exploited by several . Avdesh. Mishra, . Manisha. . Panta. , . Md. . Tamjidul. . Hoque. , Joel . Atallah. Computer Science and Biological Sciences Department, University of New Orleans. Presentation Overview. 4/10/2018. Chapters . 18.5-18.12; 20.2.2. Decision Regions and Decision Boundaries. Classifiers:. Decision trees. K-nearest neighbors. Perceptrons. Support . vector Machines (SVMs), Neural . Networks. Naïve . Bayes. R statistical computing environment RWeka Python python-wekad 1 javaArrayjavalangObject0etoSummaryString Save data in Matlab format load it back and plot it s javaObjectwekacorecon