PPT-CS548 Fall 2016 Decision Tree Showcase by Muyeedul Hoque, Chao Xu, Yue Zhao, and Kevin

Showcasing work by N Morizet NGodin JTang EMaillet MFregonese and BNormand on Classification of Acoustic Emission Signals Using Wavelets and Random Forests Application to localized corrosion. Shiqin Yan. Objective. Utilize the already existed database of the mushrooms to build a decision tree to assist the process of determine the whether the mushroom is . poisonous. .. DataSet. Existing record . Shasta College campus. Liquidambar styraciflua. , . sweetgum, liquidambar. Monoecious. —male & female flowers in separate heads; flowers small, greenish, without petals. fruit like a little spiked weapon (mace); . SVM. Sindhu Kuchipudi. INSTRUCTOR Dr.DONGCHUL KIM. OUTLINE:. Introduction. Decision-tree-based SVM.. The class separability Measure in feature space.. The Improved Algorithm For Decision-tree- Based SVM.. Watanabe. Nashali. Jeff. Clay. Caleb. Trevor. Derrius. Tim. Niya. Maddie. Shaleyka. Tyla. Michael. Charles. Kevin. Julian. Shaniyah. Hannah. Hailey. Jacob. Chris. Gavon. Angelica. Anne. Janesha. Ivy. Spring 2016 . Decision . Trees . Showcase . By . Yi . Jiang and Brandon . Boos. . ----. Showcase work by . Zhun. Yu, . Fariborz. . Haghighat. , Benjamin C.M. Fung, and Hiroshi Yoshino on . 2016 Showcase Award Guidelines . http. ://. education.qld.gov.au/community/events/showcase/2016/guidelines.html. . Preparing a 2016 Showcase nomination. What is the same as 2015?. Total submission length max. 15 A4 pages, min. 11 point . Presented . by. Jeff . Bibeau. , Max Levine, . Jie. . Gao. Showcasing Work . by. . Milos . Hauskrecht. , . Iyad. . Batal. , Michal . Valko. , . Shyam. . Visweswaran. ,. Gregory F. Cooper, Gilles Clermont.. Showcase . by . Nichole Etienne, Rohitpal Singh, Suchithra Balakrishnan, Yousef Fadila. Showcasing work by Bowen Du, Chuaren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong on “ . Catch Me If You Can - Detecting Pickpocket Suspects from Large-Scale Transit Records. Performance Monitoring Report. April 2015 – September 2015. Introduction. . The . Council is committed to measuring its performance against challenging targets. To achieve this we have developed the Annual Plan containing a set of key . Decision Tree & Bootstrap Forest C. H. Alex Yu Park Ranger of National Bootstrap Forest What not regression? OLS regression is good for small-sample analysis. If you have an extremely large sample (e.g. Archival data), the power level may aproach 1 (.99999, but it cannot be 1). . by Holly Nguyen, Hongyu Pan, Lei Shi, Muhammad Tahir. Showcasing work by . Themis P. Exarchos, Alexandros T. Tzallas, DinaBaga, Dimitra Chaloglou, Dimitrios I. Fotiadis, Sofia Tsouli, Maria Diako. u. Showcase by . Abhishek Shah, Mahdi Alouane. , Marie Solman. , Satishraju Rajendran and Eno-Obong Inyang. . Showcasing work by Cai Lile, Li Yiqun On. . ANOMALY DETECTION IN THERMAL IMAGES USING DEEP NEURAL NETWORKS. and Regress Decision Tree. KH Wong. Decision tree v3.(230403b). 1. We will learn : the Classification and Regression decision Tree ( CART) ( or . Decision Tree. ). Classification decision tree. uses. How is normal Decision Tree different from Random Forest?. A Decision Tree is a supervised learning strategy in machine learning. It may be used with both classification and regression algorithms. . As the name says, it resembles a tree with nodes. The branches are determined by the number of criteria. It separates data into these branches until a threshold unit is reached. .