'Similarity measure' presentation slideshows

SensLoc: Sensing Everyday Places and Paths using Less Energy

SensLoc: Sensing Everyday Places and Paths using Less Energy. SenSys 2010 Jeffrey. Outline. Abstract Introduction System Overview SensLoc Algorithms Evaluation Conclusions. Outline. Abstract Introduction System Overview SensLoc Algorithms Evaluation Conclusions. Abstract.

1.19k views • 111 slides

Similarity and clustering

806 views • 66 slides

CS598:Visual information Retrieval

CS598:Visual information Retrieval. Lecture III: Image Representation: Invariant Local Image Descriptors. Recap of Lecture II. Color, texture, descriptors Color histogram Color correlogram LBP descriptors Histogram of oriented gradient Spatial pyramid matching

781 views • 66 slides

713 views • 49 slides

AMCS/CS 340: Data Mining

Clustering. AMCS/CS 340: Data Mining. Xiangliang Zhang King Abdullah University of Science and Technology. Grouping fruits. Grouping apple with apple , orange with orange and banana with banana. 2. Give pictures to a computer. 3. C hange pictures to data. 4.

787 views • 49 slides

On the Utility of WordNet for Ontology Alignment: Is it Really Worth It?

On the Utility of WordNet for Ontology Alignment: Is it Really Worth It?. Uthayasanker Thayasivam and Prashant Doshi THINC Lab Thinc.cs.uga.edu Department of Computer Science University of Georgia. Outline. Introduction Background Integrating WordNet Experiments Methodology

567 views • 38 slides

3D Vision

3D Vision. CSC I6716 Spring 2004. Topic 7 of Part 2 Stereo Vision (II). Zhigang Zhu, NAC 8/203A http://www-cs.engr.ccny.cuny.edu/~zhu/VisionCourse-2004.html. Stereo Vision. Epipolar Geometry Where to search correspondences Epipolar plane, epipolar lines and epipoles

508 views • 38 slides

Data Mining Cluster Analysis: Advanced Concepts and Algorithms

778 views • 37 slides

Data Mining Cluster Analysis: Advanced Concepts and Algorithms

743 views • 37 slides

Clustering, Self_Organizing Feature Map

Clustering, Self_Organizing Feature Map. 2003. 11.3 Yoonyoung Nam Shakeel,M. YoungIn Yeo. Clustering. Outliers. Cluster1. Cluster 2. Introduction. Cluster Group of the similar objects Clustering Special method of classification Unsupervised learning : no predefined classes.

518 views • 37 slides

Data clustering, modularity optimization, and total variation on graphs

Thanks to Arjuna Flenner , Ekaterina Merkurjev , Tijana Kostic , Huiyi Hu, Allon Percus , Mason Porter, Thomas Laurent. Andrea Bertozzi University of California, Los Angeles. Data clustering, modularity optimization, and total variation on graphs. Diffuse interface methods.

560 views • 37 slides

An Introduction to Latent Semantic Analysis

An Introduction to Latent Semantic Analysis. Melanie Martin October 14, 2002 NMSU CS AI Seminar. Acknowledgements. Peter Foltz for conversations, teaching me how to use LSA, pointing me to the important work in the field. Thanks!!! ARL Grant for supporting this work. Outline. The Problem

939 views • 35 slides

Discovering Pictorial Brand Associations from Large-Scale Online Image Data

Discovering Pictorial Brand Associations from Large-Scale Online Image Data. Gunhee Kim* Eric P. Xing. School of Computer Science, Carnegie Mellon University *: (now with Disney Research Pittsburgh). December 7, 2013. Outline. Problem Statement Dataset Algorithm

695 views • 34 slides

Using a Trust Network To Improve Top-N Recommendation

Mohsen Jamali , Martin Ester Simon Fraser University Vancouver, Canada. Using a Trust Network To Improve Top-N Recommendation. ACM RecSys 2009. Outline. Introduction Collaborative Filtering Approaches User based CF Item based CF Trust based Approaches Random Walk Combined Experiments

524 views • 32 slides

Generation of Attribute Value Taxonomies from Data for Data-Driven Construction of Accurate and Compact Classifiers

Generation of Attribute Value Taxonomies from Data for Data-Driven Construction of Accurate and Compact Classifiers. Dae-Ki Kang , Adrian Silvescu, Jun Zhang and Vasant Honavar Artificial Intelligence Research Laboratory Iowa State University, USA.

487 views • 31 slides

Data Mining: Introduction and a Health Care Application

Data Mining: Introduction and a Health Care Application. Prem Swaroop (pswaroop@rhsmith.umd.edu) and Dr Bruce Golden (bgolden@rhsmith.umd.edu) Robert H Smith School of Business, University of Maryland, College Park. Data Mining: Introduction. Motivation: Why mine data

596 views • 26 slides

Segmentation Analysis

Segmentation Analysis. Segmentation. Assign people to groups/clusters on basis of similarities in characteristics, attributes Bases of segmentation: - Demographic - Behavior - Geographic - Benefit - Product/ Product usage - Purchase

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540 views • 19 slides

Characteristic Identifier Scoring and Clustering for Email Classification

Characteristic Identifier Scoring and Clustering for Email Classification. By Mahesh Kumar Chhaparia. Email Clustering. Given a set of unclassified emails, the objective is to produce high purity clusters keeping the training requirements low. Outline:

304 views • 13 slides

Max-margin Clustering: Detecting Margins from Projections of Points on Lines

Max-margin Clustering: Detecting Margins from Projections of Points on Lines. Raghuraman Gopalan 1 , and Jagan Sankaranarayanan 2 1 Center for Automation Research, University of Maryland, College Park, MD USA 2 NEC Labs, Cupertino, CA USA E-mail: { raghuram,jagan }@umiacs.umd.edu .

215 views • 9 slides