'Edge weights' diaporamas de présentation

CSE 326: Data Structures Graph Algorithms Graph Search Lecture 23

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Topic-Sensitive SourceRank: Extending SourceRank for Performing Context-Sensitive Search over Deep-Web

Topic-Sensitive SourceRank: Extending SourceRank for Performing Context-Sensitive Search over Deep-Web. MS Thesis Defense Manishkumar Jha Committee Members Dr. Subbarao Kambhampati Dr. Huan Liu Dr. Hasan Davulcu. Deep Web Integration Scenario.

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CS420 lecture twelve Shortest Paths

CS420 lecture twelve Shortest Paths. wim bohm cs csu. Shortest Paths Problems. Given a weighted directed graph G=(V,E) find the shortest path path length is the sum of its edge weights. The shortest path (SP) from u to v is ? if there is no path from u to v . .

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Conjugate gradients, sparse matrix-vector multiplication, graphs, and meshes

Conjugate gradients, sparse matrix-vector multiplication, graphs, and meshes. Thanks to Aydin Buluc , Umit Catalyurek , Alan Edelman, and Kathy Yelick for some of these slides. T he middleware of scientific computing. Continuous physical modeling. Ax = b. Linear algebra. Computers.

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Range Queries in Distributed Networks

Range Queries in Distributed Networks. Jie Gao Stony Brook University Dagstuhl Seminar, March 14 th , 2011. Target Tracking with Sensor Networks. Target Queries. Range queries : # targets within any geographic range R. Tracking queries : Find the yellow car.

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Phylogenetic Trees

Phylogenetic Trees. Phylogeny. PHYLOGENY (coined 1866 Haeckel) the line of descent or evolutionary development of any plant or animal species the origin and evolution of a division, group or race of animals or plants. Goals. Understand evolutionary history Origin of Europeans

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Phylogenetic Trees

Phylogenetic Trees. Phylogeny. PHYLOGENY (coined 1866 Haeckel) the line of descent or evolutionary development of any plant or animal species the origin and evolution of a division, group or race of animals or plants. Goals. Understand evolutionary history Origin of Europeans

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CSM6120 Introduction to Intelligent Systems

CSM6120 Introduction to Intelligent Systems. Search 2. Search. Many of the tasks underlying AI can be phrased in terms of a search for the solution to the problem at hand Need to be able to represent the task in a suitable manner

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Background

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Minimum Spanning Trees

Minimum Spanning Trees. Problem: Connect a set of nodes by a network of minimal total length Some applications: Communication networks Circuit design Layout of highway systems. Motivation: Minimum Spanning Trees.

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Optical Mapping as a Method of Whole Genome Analysis

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Compact Representations of Coalitional Games

Compact Representations of Coalitional Games. Jianing Yu. Outline. Computational problems associated with a solution concept Why we need the compact representation of coalitional games Compactly-represented coalitional games Weighted graph game

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Learning Trajectory Patterns by Clustering: Comparative Evaluation

Learning Trajectory Patterns by Clustering: Comparative Evaluation. Group D. Problem Description & Definition. Problem Description & Definition. Preprocessing G rid Q uantization Clustering

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Design and Analysis of Algorithms Minimum Spanning trees

Design and Analysis of Algorithms Minimum Spanning trees. Haidong Xue Summer 2012, at GSU. What are trees in undirected graphs?. We are familiar with a rooted directed tree, such as: Binary search tree Heap They look like: You know which one the root is Who the parent is

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Data Access Profiling & Improved Structure Field Regrouping in Pegasus

Data Access Profiling & Improved Structure Field Regrouping in Pegasus . Vas Chellappa & Matt Moore May 2, 2005 / Optimizing Compilers / Project Poster Session. Introduction. Structure definitions group fields by semantics, not access contemporaneity

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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Authors: M. Belkin , P. Niyogi and V. Sindhwani Journal of Machine Learning Research, 2006 Presented by: Huy Tho Ho. Overview. Introduction Reproducing Kernel Hilbert Space

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Analysis of Algorithms

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Weighted graphs

Weighted graphs. 12. Example Consider the following graph, where nodes represent cities, and edges show if there is a direct flight between each pair of cities. CHG SF HTD

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Sequoia: Virtual-Tree Models for Internet Path Metrics

Sequoia: Virtual-Tree Models for Internet Path Metrics. Rama Microsoft Research. Also: Ittai Abraham (Hebrew Univ.) Mahesh Balakrishnan (Cornell) Archit Gupta (Univ. Wisc .) Fabian Kuhn (EPFL) Dahlia Malkhi (MSR) Kunal Talwar (MSR). Introduction.

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Input Documents

Document Categorization and Related Concepts Prediction using Wikipedia Articles, Category Network and Page Links Graph Zareen Saba Syed zarsyed1@umbc.edu Wikipedia The Free Encyclopedia

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