'Smallest distance' presentation slideshows

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????? ?? ? ????? ?? ? ??? ???????? ? ???????????. ? ? ???? ??? ? ? ? ? ??? ?? ??? ???? ?? ? ?. ????? ? ???? ? ? ?? ? 3. Travel with NASA from the biggest to the smallest distance of the universe.

556 views • 47 slides

Travel with NASA from the biggest to the smallest distance of the universe.

Travel with NASA from the biggest to the smallest distance of the universe. This is a journey which starts and ends in distances difficult for the human mind to capture . It starts from 10 million light years (10 23 m.) at space, and ends at 100 atom/meter (10 -16 m.) on Earth.

471 views • 46 slides

Travel with NASA from the biggest to the smallest distance of the universe.

Travel with NASA from the biggest to the smallest distance of the universe. This is a journey which starts and ends in distances difficult for the human mind to capture. It starts from 10 million light years (10 23 m.) at space, and ends at 100 atom/meter (10 -16 m.) on Earth.

637 views • 45 slides

Travel with NASA from the biggest to the smallest distance of the universe.

Travel with NASA from the biggest to the smallest distance of the universe. This is a journey which starts and ends in distances difficult for the human mind to capture . It starts from 10 million light years (10 23 m.) at space, and ends at 100 atom/meter (10 -16 m.) on Earth.

846 views • 45 slides

Sparse Kernel Machines

Sparse Kernel Machines. Introduction. Kernel function k(xn,xm) must be evaluated for all pairs of training points Advantageous (computationally) to look at kernel algorithms that have sparse solutions new predictions depend only on kernel function evaluated at a few training points

627 views • 39 slides

p is an ( r,M)-coreobject iff |D(p, r)|? M (e.g. M=4, r = ) x is directly density reachable from a coreobject , p, iff x?D(p,r) (disk of radius r about p) THM: if p i , p i+1 are coreobjects and p i+1 is DDR from p i then p i is DDR from p i+1

451 views • 38 slides

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

646 views • 37 slides

Section 12: Graph Algorithms

Section 12: Graph Algorithms. Shortest Paths. Given a weighted graph, the shortest path between two vertices is the path between them whose edges have the lowest sum. Useful for: Finding cheap airline itineraries Finding fast airline itineraries Network routing. Shortest Path.

351 views • 23 slides

High Resolution Snow Analysis for COSMO

High Resolution Snow Analysis for COSMO. jean-marie.bettems@meteoswiss.ch COSMO General Meeting 18-21 September 2007. 20040310. Satellite data. Near real time, high resolution, composite, partial snow cover Based on: Meteosat SEVIRI. 20070325. Snow depth analysis .

318 views • 19 slides

Microbiology

Microbiology. Definition: Microbiology is the science that deals with microorganisms that can not be seen by the naked eye . The Importance of Microbiology : 1-The Environment : Microbes are responsible for the cycling of carbon, nitrogen and phosphorus .

836 views • 16 slides

A Traffic Simulation Model Allowing For Wide-ranged Vehicle Communication

Timmy Galvin. A Traffic Simulation Model Allowing For Wide-ranged Vehicle Communication . Abstract. Traffic Simulation Moving past optimal Model Types: Fluid Flow Agent Based Modeling. Introduction. Communication Calculation- information flow Human behavior Reaction and Response.

230 views • 10 slides

MASS SPECTROMETRY MATLAB SIMULATION

MASS SPECTROMETRY MATLAB SIMULATION. Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University. Cross Correlation Issues. We created the database using matrices, where column 1 is the mass to charge ratio and column 2 is the intensity percentage.

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Quality of Clusterings

Quality of Clusterings. Two metrics: SSE Dissimilarity Ratio. Computing SSE. Save clusters. Two new columns are created: Cluster and Distance . Create new column as formula. Name it as dist-sqr and define it as Distance 2

125 views • 4 slides