'Nearest neighbor' presentation slideshows

1.26k views • 93 slides

Convex Hull Problem

Convex Hull Problem. Presented By Erion Lin. Outline. Convex Hull Problem Voronoi Diagram Fermat Point. Outline. Convex Hull Problem Voronoi Diagram Fermat Point. Convex Hull Problem. Definitions Algorithm . Definitions. Concave Polygon. Convex Polygon. A Convex Hull.

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DATA MINING LECTURE 10b

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Geometric Operations

Geometric Operations. Move over rover. Geometric Operations. Previous operations have taken a sample at some location and changed the sample value (the light intensity ) but left the location unchanged.

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Locality sensitive hashing (LSH)

Locality sensitive hashing (LSH). Nearest Neighbor. Given a set P of n points in R d. Nearest Neighbor. Want to build a data structure to answer nearest neighbor queries. Voronoi Diagram. Build a Voronoi diagram & a point location data structure. Curse of dimensionality.

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February 11 2013 Geoffrey Fox gcf@indiana.edu

X-Informatics Case Study: e-Commerce and Life Style Informatics: Recommender Systems III: Algorithms Continued and Software. February 11 2013 Geoffrey Fox gcf@indiana.edu http:// www.infomall.org/X-InformaticsSpring2013/index.html

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Big Data

Big Data. Lecture 6: Locality Sensitive Hashing (LSH). Nearest Neighbor . Given a set P of n points in R d. Nearest Neighbor . Want to build a data structure to answer nearest neighbor queries. Voronoi Diagram. Build a Voronoi diagram & a point location data structure.

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Chernobyl Nuclear Power Plant Explosion

Chernobyl Nuclear Power Plant Explosion. Analysis of Long Term Effects on Land Use Patterns. Introduction. April of 1986 a nuclear accident damaged a reactor at the Chernobyl nuclear power plant in Ukraine. released substantial radioactive material into the environment (NRC.gov ).

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Nearest Neighbor POINT LOCATION Using KDTree on GPU

By Shawn Brown. Nearest Neighbor POINT LOCATION Using KDTree on GPU. Terrain Visualization: Want to compare results of 2 different surface generation methods against baseline for error Baseline (C) contains ~100 million terrain points Method A & B each generate ~100 million points

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????? ???? -- Clustering

????? ???? -- Clustering. ?"? ??? ???????. ?????? ?????: ????? ????? ?? ?????. ??? ????? ????? ????? Regression, Classification (Supervised), k- nn Clustering (Unsupervised) k- meand Partitioning Algorithms (k-mean), Hierarchical Algorithms ????? ??????: ??? ?????? "?????" ???? Euclidean

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QUERYING IMPRECISE DATA IN MOVING OBJECT ENVIRONMENT

REYNOLD CHENG. DMITRI V.KALASHNIKOV. SUNIL PRABHAKAR. PRESENTED BY: ... Example: If there is a time delay between capture of location and its script at ...

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An Introduction to The Baltimore Neighborhood Indicators Alliance and Geographic Information Systems

An Introduction to The Baltimore Neighborhood Indicators Alliance and Geographic Information Systems. Cheryl Knott GIS Analyst BNIA-JFI University of Baltimore June 30, 2009. Topics:. BNIA-JFI Background NNIP Services Data Vital Signs Other Geographic Information Systems (GIS)

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Classification Techniques II

Classification Techniques II. Nearest Neighbor Classifiers Support Vector Machines Editing and Condensing Techniques for NN Classifiers Ensemble Methods and Adaboost Model Evaluation . Classification and Decision Boundaries.

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Traveling salesperson problem

Traveling salesperson problem. Chapter 4 sec. 2. What is the traveling salesperson problem?. A famous and difficult problem to solve in graph theory. Tools we need to solve this problem. 1. Hamiltonian path- A path that passes through all the vertices of a graph exactly once.

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INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID

INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID. Lecture # 43 Classification. ACKNOWLEDGEMENTS. The presentation of this lecture has been taken from the following sources

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Recognizing Human Activities from Multi-Modal Sensors

Recognizing Human Activities from Multi-Modal Sensors. Shu Chen,Yan Huang Department of Computer Science & Engineering University of North Texas Denton, TX 76207, USA. Content. Introduction Related Work Overview Preparation Data Collection Preprocessing Experimental result Conclusion.

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Objectives: Density Estimation Parzen Windows k-Nearest Neighbor Properties of Metrics

LECTURE 14: NONPARAMETRIC TECHNIQUES. Objectives: Density Estimation Parzen Windows k-Nearest Neighbor Properties of Metrics Resources: DHS: Chapter 4 (Part 1) DHS: Chapter 4 (Part 2) EB&OS: Parzen Windows MS: Nonparametric Density KT: K-Nearest Neighbor Rule SOSS: KNN Tutorial. Audio:.

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Breast Cancer Classification Using ANN

Breast Cancer Classification Using ANN. Ross Nordstrom Intro to Artificial Neural Networks Yu Hen Hu December 14, 2010. Overview. Learning Methods K-Nearest Neighbor Maximum Likelihood (using negative log) Training Methods Leave One Out

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Algorithms for Data Analytics

Algorithms for Data Analytics. Chapter 3. Plans. Introduction to Data-intensive computing (Lecture 1) Statistical Inference: Foundations of statistics (Chapter 2) (Lecture 2) This week we will look at Algorithms for data analytics (Chapter 3)

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Information Retrieval and Organisation

Information Retrieval and Organisation. Revision. Dell Zhang Birkbeck, University of London. Scope: Language Modeling. 11 Probabilistic Information Retrieval 11.1 Review of Basic Probability Theory 12 Language M odels for I nformation R etrieval 12.1 Language M odels

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