'Distance metrics' presentation slideshows

Distance Metric Learning: A Comprehensive Survey

Distance Metric Learning: A Comprehensive Survey. Liu Yang Advisor: Rong Jin May 8th, 2006. Outline. Introduction Supervised Global Distance Metric Learning Supervised Local Distance Metric Learning Unsupervised Distance Metric Learning Distance Metric Learning based on SVM

1.53k views • 51 slides

Distance functions and IE -2

470 views • 46 slides

Efficient Clustering of High-Dimensional Data Sets

Efficient Clustering of High-Dimensional Data Sets. Andrew McCallum WhizBang! Labs & CMU. Kamal Nigam WhizBang! Labs. Lyle Ungar UPenn. Large Clustering Problems. Many examples Many clusters Many dimensions. Example Domains. Text Images Protein Structure.

552 views • 43 slides

Machine Learning Lecture 9: Clustering

Machine Learning Lecture 9: Clustering. Moshe Koppel Slides adapted from Raymond J. Mooney. Clustering. Partition unlabeled examples into disjoint subsets of clusters , such that: Examples within a cluster are very similar Examples in different clusters are very different

549 views • 40 slides

Nearest Neighbor

642 views • 37 slides

Database Research: Data Mining & Other Areas

Database Research: Data Mining & Other Areas. Dr. Aparna Varde Ph.D., Computer Science, WPI, MA Assistant Professor, Computer Science, VSU, VA Presentation at Montclair State University, NJ May 2, 2008. Agenda. Database Systems Introduction to Databases and Research Areas Data Mining

468 views • 35 slides

CISC 4631 Data Mining

CISC 4631 Data Mining. Lecture 03: Nearest Neighbor Learning Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook authors) Prof . R. Mooney (UT Austin) Prof E. Keogh (UCR), Prof . F. Provost (Stern, NYU). Compute Distance. Test Record. Training Records.

430 views • 35 slides

CISC 4631 Data Mining

CISC 4631 Data Mining. Lecture 03: Nearest Neighbor Learning Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook authors) Prof . R. Mooney (UT Austin) Prof E. Keogh (UCR), Prof . F. Provost (Stern, NYU). Compute Distance. Test Record. Training Records.

432 views • 35 slides

Distance functions and IE

354 views • 35 slides

More announcements

More announcements. Unofficial auditors: send email to Sharon Woodside (sharonw@cs) to make sure you get any late-breaking announcements. Project: Already one question! Strongly prefer groups of 2-4 people. Relevant software systems: SecondString (Java string matching/learning)

332 views • 33 slides

Cluster Analysis

Cluster Analysis. Cluster Analysis. What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Clustering Methods Outlier Analysis Summary .

1.78k views • 31 slides

Cluster Analysis

Cluster Analysis. Cluster Analysis. What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Clustering Methods Outlier Analysis Summary .

908 views • 31 slides

R outing S tate D istance: A Path-based Metric for Network Analysis

389 views • 30 slides

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319 views • 21 slides

Machine Learning Lecture 11: Nearest Neighbor

Machine Learning Lecture 11: Nearest Neighbor. Moshe Koppel Slides adapted from Raymond J. Mooney. Instance-Based Learning.

310 views • 14 slides

CS 391L: Machine Learning: Instance Based Learning

CS 391L: Machine Learning: Instance Based Learning. Raymond J. Mooney University of Texas at Austin. Instance-Based Learning.

268 views • 13 slides

96 views • 3 slides

190 views • 1 slides