'Class label' presentation slideshows

Lecture 3-4: Classification & Clustering

Lecture 3-4: Classification & Clustering. Both operate a partitioning of the parameter space . Clustering: predictive value Classification : descriptive value. Both clustering and classification aim at partitioning a dataset into subsets that bear similar characteristics.

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Data Mining Techniques for Malware Detection R. K. Agrawal School of Computer and Systems Sciences

Data Mining Techniques for Malware Detection R. K. Agrawal School of Computer and Systems Sciences Jawaharlal Nehru University NewDelhi-110067. Outline. Data Mining Classification Clustering Association Rules Experimental Results Conclusion and Future Work.

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Learning Local Affine Representations for Texture and Object Recognition

Learning Local Affine Representations for Texture and Object Recognition. Svetlana Lazebnik Beckman Institute, University of Illinois at Urbana-Champaign (joint work with Cordelia Schmid, Jean Ponce). Overview. Goal: Recognition of 3D textured surfaces, object classes Our contribution:

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Using linked data to interpret tables

Using linked data to interpret tables. Varish Mulwad , Tim Finin , Zareen Syed and Anupam Joshi University of Maryland, Baltimore County November 8, 2010. Interpreting a table. http://dbpedia.org/class/yago/NationalBasketballAssociationTeams. dbprop:team.

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Data Mining

Data Mining. IS698 Min Song. Introduction. Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems Data Mining Task Primitives

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How to do Bayes-Optimal Classification with Massive Datasets: Large-scale Quasar Discovery

How to do Bayes-Optimal Classification with Massive Datasets: Large-scale Quasar Discovery. Alexander Gray Georgia Institute of Technology College of Computing Joint work with Gordon Richards (Princeton), Robert Nichol (Portsmouth ICG), Robert Brunner (UIUC/NCSA), Andrew Moore (CMU).

555 views • 42 slides

Bayesian Classification

Bayesian Classification. Instructor: Qiang Yang Hong Kong University of Science and Technology Qyang@cs.ust.hk Thanks: Dan Weld, Eibe Frank. Weather data set. Windy=True. Windy=False. Play=yes. Play=no. Basics. Unconditional or Prior Probability Pr(Play=yes) + Pr(Play=no)=1

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Fuzzy Support Vector Machines (FSVM s )

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Example of a Decision Tree

continuous. categorical. categorical. class. Example of a Decision Tree. Splitting Attributes. Refund. Yes. No. NO. MarSt. Married. Single, Divorced. TaxInc. NO. < 80K. > 80K. YES. NO. Model: Decision Tree. Training Data. NO. Another Example of Decision Tree. continuous.

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I: Introduction to Data Mining A. Preview Data Mining B. A more detailed Introduction C. Course Information

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A Boosting Algorithm for Classification of Semi-Structured Text

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A Hierarchy of Independence Assumptions for Multi-Relational Bayes Net Classifiers

School of Computing Science Simon Fraser University Vancouver, Canada. A Hierarchy of Independence Assumptions for Multi-Relational Bayes Net Classifiers. Outline. Multi- R elational Classifiers Multi-Relational Independence Assumptions Classification Formulas Bayes Nets Evaluation.

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Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 11, Beyond supervised and unsupervised learning of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Semi-supervised and multi-instance learning. Semisupervised learning

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CMSC 671 Fall 2005

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Tackling the Poor Assumptions of Naive Bayes Text Classifiers Pubished by: Jason D.M.Rennie, Lawrence Shih, Jamime Teeva

Tackling the Poor Assumptions of Naive Bayes Text Classifiers Pubished by: Jason D.M.Rennie, Lawrence Shih, Jamime Teevan, David R.Karger . Liang Lan 11/19/2007. Outline. Introduce the Multinomial Naive Bayes Model for Text Classification. The Poor Assumption of Multinomial Naive Bayes Model.

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Shuang-Hong Yang, Hongyuan Zha, Bao-Gang Hu NIPS2009

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4. Maximum Likelihood

4. Maximum Likelihood. Prof. A.L. Yuille Stat 231. Fall 2004. Learning Probability Distributions. Learn the likelihood functions and priors from datasets. Two Main Strategies. Parametric and Non-Parametric. This Lecture and the next will concentrate on Parametric methods.

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Final Project Specifications

Final Project Specifications. CMPE 650. KNN Euclidean classifier. Finds K- nearest neighbors to the test input and decides based on the majority vote of the neighbors. utilizes Euclidean distance. X Test. Simulation and verification setup.

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