'Regression tree' presentation slideshows

Last lecture summary

Last lecture summary. Information theory. quantifies information, information is inherently linked with uncertainty and surprise. Consider a random variable and ask how much information is received when a specific value for this variable is observed.

714 views • 64 slides

Efficient Learning of Statistical Relational Models

627 views • 45 slides

Agenda

Agenda. Bayes rule Popular classification methods Logistic regression Linear discriminant analysis (LDA)/QDA and Fisher criteria K-nearest neighbor (KNN) Classification and regression tree (CART) Bagging Boosting Random Forest Support vector machines (SVM)

572 views • 44 slides

Visualization

Visualization. Blaz Zupan Faculty of Computer & Info Science University of Ljubljana, Slovenia. Visual Data Mining. Basic idea visual presentation of the data gain insight & generate hypothesis draw conclusions directly interact with data Include human in the data exploration process

702 views • 42 slides

Qualitative Reverse Engineering

Qualitative Reverse Engineering. Do ri an Šuc and Ivan Bratko Artificial Intelligence Laboratory Faculty of Computer and Information Science University of Ljubljana, Slovenia. Overview. Qualitative reverse engineering: generating explanation of engineering designs in qualitative terms

925 views • 41 slides

Additive Models ? Trees ? and Related Models

Additive Models ? Trees ? and Related Models. Prof. Liqing Zhang Dept. Computer Science & Engineering, Shanghai Jiaotong University. Introduction. 9.1: Generalized Additive Models 9.2: Tree-Based Methods 9.3: PRIM:Bump Hunting

588 views • 39 slides

Health in the Workplace

Health in the Workplace. Transforming Healthcare Delivery: Advancing Multidisciplinary Research at Purdue University Healthy Lifestyles and Healthy Workplaces. James D. McGlothlin, M.P.H., Ph.D., C.P.E Balmatee Bidassie, MS, MA Irene Mena, MS School of Health Science, Purdue University

490 views • 38 slides

Ensemble Learning (2), Tree and Forest

Ensemble Learning (2), Tree and Forest. Classification and Regression Tree Bagging of trees Random Forest. Motivation. To estimate complex response surface and class boundary. Reminder: SVM achieves this goal by the kernel trick; Boosting achieves this goal by combining weak classifiers

482 views • 30 slides

433 views • 27 slides

Dependency networks

Dependency networks. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576 sroy@biostat.wisc.edu Nov 26 th , 2013. Goals for today. Introduction to Dependency networks GENIE3: A network inference algorithm for learning a dependency network from gene expression data

403 views • 26 slides

Estimation As A Data Mining Task

Estimation As A Data Mining Task. Theoretical Understanding. Vinh Ngo & Mike Ellis. Introduction. Estimation Predicting values not in predetermined categories Three main techniques Regression Decision trees Neural networks. Regression. Linear regression Method of Least Squares

353 views • 22 slides

333 views • 21 slides

How can we improve the infrared atmospheric correction algorithm?

How can we improve the infrared atmospheric correction algorithm?. Peter J Minnett Meteorology & Physical Oceanography Sareewan Dendamrongvit Miroslav Kubat Department of Electrical & Computer Engineering University of Miami. NLSST.

364 views • 21 slides

Motivation

Empirical Algorithmics Reading Group Oct 11, 2007 Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach by Thomas Bartz-Beielstein & Sandor Markon Presenter: Frank Hutter. Motivation.

306 views • 18 slides

Motivation

Empirical Algorithmics Reading Group Oct 11, 2007 Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach by Thomas Bartz-Beielstein & Sandor Markon Presenter: Frank Hutter. Motivation.

263 views • 18 slides

Lab: SVM (various libraries) and a little on trees

210 views • 13 slides

14 January 2009

14 January 2009. 2009 AMS Artificial Intelligence Conference A Data Mining Approach to Soil Temperature and Moisture Prediction. Bill Myers Seth Linden, Gerry Wiener. Project Overview and Goals. Improve soil temperature and moisture prediction Integrate and Evaluate NASA-MODIS data sets

185 views • 13 slides

Recitation 4 M ay 23

137 views • 6 slides

ASSESSING SEARCH TERM STRENGTH IN SPOKEN TERM DETECTION Amir Harati and Joseph Picone

ASSESSING SEARCH TERM STRENGTH IN SPOKEN TERM DETECTION Amir Harati and Joseph Picone Institute for Signal and Information Processing, Temple University. www.isip.piconepress.com. Introduction Searching audio, unlike text data , is approximate and is based on likelihoods

120 views • 1 slides

Experimentation Duration is the most significant feature with around 40% correlation.

ASSESSING SEARCH TERM STRENGTH IN SPOKEN TERM DETECTION Amir Harati and Joseph Picone Institute for Signal and Information Processing, Temple University. www.isip.piconepress.com. Introduction Searching audio, unlike text data , is approximate and is based on likelihoods.

109 views • 1 slides