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Pattern Recognition: Statistical and Neural

Pattern Recognition: Statistical and Neural

Nanjing University of Science & Technology. Pattern Recognition: Statistical and Neural. Lonnie C. Ludeman Lecture 27 Nov 9, 2005. Lecture 27 Topics. K-Means Clustering Algorithm Details K-Means Step by Step Example ISODATA Algorithm -Overview

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612 views • 37 slides
Kinect-based Image Segmentation

Kinect-based Image Segmentation

Kinect-based Image Segmentation. Presenter: Ali Pouryazdanpanah Professor: Dr. Brendan Morris University of Nevada, las vegas. Overview Intuition and basic algorithms (k-means) Advanced algorithms (spectral clustering ) Extend the methods for Kinect device. Clustering: intuition.

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486 views • 35 slides
MapReduce TG11 BOF FutureGrid Team (Geoffrey Fox)

MapReduce TG11 BOF FutureGrid Team (Geoffrey Fox)

MapReduce TG11 BOF FutureGrid Team (Geoffrey Fox). TG11 19 July 2011 Downtown Marriott Salt Lake City. MapReduce@TeraGrid /XSEDE/FutureGrid.

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340 views • 22 slides
Hierarchical Stability Based Model Selection for Data Clustering

Hierarchical Stability Based Model Selection for Data Clustering

Hierarchical Stability Based Model Selection for Data Clustering. Bing Yin Advisor: Greg Hamerly. Roadmap What is clustering? What is model selection for clustering algorithms? Stability Based Model Selection: Proposals and Problems Hierarchical Stability Based Model Selection ? Algorithm

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305 views • 19 slides
People

People

Cluster Analysis. Variables. Customers are grouped by common characteristics. People. K-means. Minimize squared error, the distance between points labeled to be in a cluster and a point designated as the center of that cluster. K-means Clustering.

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270 views • 12 slides

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