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Title:

Den lustigen Berlinern gewidmet. Auf der Pferdebahn (Nach dem beliebten Couplet des Theatre Americain). Polka. Achte Auflage.

Description:

da capo, with vocal trio ; piano and voice First line of trio Ja man knupft gemuthlich auf der Pferdebahn ; ads on back cover for Carl Simon stock ; 331 ; Johns Hopkins University, Levy Sheet Music Collection, Box 061, Item 003 ; Componirt von Carl Meyer.

da capo, with vocal trio ; piano and voice First line of trio Ja man knupft gemuthlich auf der Pferdebahn ; ads on back cover for Carl Simon stock ; 331 ; Johns Hopkins University, Levy Sheet Music Collection, Box 061, Item 003 ; Componirt von Carl Meyer. Minimize

Publisher:

Carl Simon, W.58 Friedrichstr. (an der Leipzigerstr.)

Contributors:

Componirt von Carl Meyer (composer)

Year of Publication:

[n.d.]

Subjects:

Carriages & coaches ; Buses ; Horses ; Transportation

Carriages & coaches ; Buses ; Horses ; Transportation Minimize

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Title:

Offense-Defense Approach to Ranking Team Sports

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Description:

The rank of an object is its relative importance to the other objects in the set. Often a rank is an integer assigned from the set 1,.,n. A ranking model is a method of determining a way in which the ranks are assigned. Usually a ranking model uses information available on the objects to determine their respective ratings. The most recognized ap...

The rank of an object is its relative importance to the other objects in the set. Often a rank is an integer assigned from the set 1,.,n. A ranking model is a method of determining a way in which the ranks are assigned. Usually a ranking model uses information available on the objects to determine their respective ratings. The most recognized application of ranking is the competitive sports. Numerous ranking models have been created over the years to compute the team ratings for various sports. In this paper we propose a flexible, easily coded, fast, iterative approach we call the Offense-Defense Model (ODM), to generating team ratings. The convergence of the ODM is grounded in the theory of matrix balancing. Minimize

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article

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Title:

Die invloed van toegevoegde metaalkatione op die reduseerbaarheid van [a]-hematiet

Description:

Please refer to full text to view abstract ; M.Sc. (Chemistry)

Please refer to full text to view abstract ; M.Sc. (Chemistry) Minimize

Contributors:

Van Berge, P.C., Prof.

Year of Publication:

2014-03-18

Document Type:

Thesis

Language:

afr

Subjects:

Hematite ; Reduction (Chemistry)

Hematite ; Reduction (Chemistry) Minimize

Rights:

University of Johannesburg

University of Johannesburg Minimize

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Title:

Uncoupling The Perron Eigenvector Problem

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Description:

For a nonnegative irreducible matrix A with spectral radius # , this paper is concerned with the determination of the unique normalized Perron vector ## # which satisfies A ## # = # ## # , ## # > 0 , P j # j = 1 . It is explained how to uncouple a large matrix A into two or more smaller matrices --- say P 11 , P 22 , · · · , P kk --- such that t...

For a nonnegative irreducible matrix A with spectral radius # , this paper is concerned with the determination of the unique normalized Perron vector ## # which satisfies A ## # = # ## # , ## # > 0 , P j # j = 1 . It is explained how to uncouple a large matrix A into two or more smaller matrices --- say P 11 , P 22 , · · · , P kk --- such that this sequence of smaller matrices has the following properties. . Each P ii is also nonnegative and irreducible so that each P ii has a unique Perron vector ## # (i) . . Each P ii has the same spectral radius, # , as A . . It is possible to determine the ## # (i) 's completely independent of each other so that one can execute the computation of the ## # (i) 's in parallel. . It is possible to easily couple the smaller Perron vectors ## # (i) back together in order to produce the Perron vector ## # for the original matrix A . UNCOUPLING THE PERRON EIGENVECTOR PROBLEM Carl D. Meyer + 1. INTRODUCTION For a nonnegative irreducible. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-12

Source:

http://meyer.math.ncsu.edu/Meyer/PS_Files/UncouplingPerronEvector.ps

Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Orthogonal Reduction On Vector Computers

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Description:

This paper concerns the implementation of the QR factorization by Givens and Householder transformations on vector computers . Following the analysis of Dongarra, et al. [1984] for Gaussian elimination, various ijk forms for both Givens and Householder transformations are investigated. Conclusions concerning which of these forms have desirable o...

This paper concerns the implementation of the QR factorization by Givens and Householder transformations on vector computers . Following the analysis of Dongarra, et al. [1984] for Gaussian elimination, various ijk forms for both Givens and Householder transformations are investigated. Conclusions concerning which of these forms have desirable or undesirable properties for vector computers are presented. These ijk forms utilize only rows or columns as the basic entities of computation. Data flow organizations such as given by Bojanczyk, et al. [1984] or the various annihilation patterns for Givens method such as those considered by Modi and Clarke [1984] are not considered. Furthermore, the pipelined Givens and the windowed Householder schemes developed for parallel machines by Dongarra, Sameh, and Sorensen [1986] are not discussed. We report on experiments run on a CRAY-1 and conclude from these experiments together with our analysis which of the ijk forms are most promising on this machine. This work complements the results of Dongarra, Kaufman, and Hammarling [1986]. 2. THE ijk FORMS OF HOUSEHOLDER REDUCTION. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-11

Source:

http://meyer.math.ncsu.edu/Meyer/PS_Files/OrthogRedVectorComp.ps

Document Type:

text

Language:

en

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Determining the Number of Clusters via Iterative Consensus Clustering ∗

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We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition probability matrix are used to d...

We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition probability matrix are used to determine the number of clusters. For noisy or high-dimensional data, an iterative technique is presented to refine this consensus matrix in way that encourages a block-diagonal form. It is shown that the resulting consensus matrix is generally superior to existing similarity matrices for this type of spectral analysis. 1 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-09-23

Source:

http://meyer.math.ncsu.edu/Meyer/PS_Files/SIAM SDM2013.pdf

Document Type:

text

Language:

en

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Journal of Quantitative Analysis in Sports Manuscript 1210 Rush versus Pass: Modeling the NFL

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Description:

A common question in football is whether a strong rushing or passing offense is more important in determining the outcome of a game. On the surface, it is easy to see both sides of the debate. A powerful running game tends to slowly and deliberately advance the ball down the field, using large amounts of time

A common question in football is whether a strong rushing or passing offense is more important in determining the outcome of a game. On the surface, it is easy to see both sides of the debate. A powerful running game tends to slowly and deliberately advance the ball down the field, using large amounts of time Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-02-27

Source:

http://meyer.math.ncsu.edu/Meyer/REU/REU2009/REU2009Paper.pdf

Document Type:

text

Language:

en

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Title:

Data Clustering via Dimension Reduction and Algorithm Aggregation

Description:

We focus on the problem of clustering large textual data sets. We present 3 well-known clustering algorithms and suggest enhancements involving dimension reduction. We propose a novel method of algorithm aggregation that allows us to use many clustering algorithms at once to arrive on a single solution. This method helps stave off the inconsiste...

We focus on the problem of clustering large textual data sets. We present 3 well-known clustering algorithms and suggest enhancements involving dimension reduction. We propose a novel method of algorithm aggregation that allows us to use many clustering algorithms at once to arrive on a single solution. This method helps stave off the inconsistency inherent in most clustering algorithms as they are applied to various data sets. We implement our algorithms on several large benchmark data sets. Minimize

Contributors:

Ernest Stitzinger, Committee Member ; Carl Meyer, Committee Chair ; Ilse Ipsen, Committee Member

Year of Publication:

2008-11-07

Subjects:

dimension reduction ; nonnegative matrix factorization ; document clustering ; data clustering ; singular value decomposition ; clustering algorithms

dimension reduction ; nonnegative matrix factorization ; document clustering ; data clustering ; singular value decomposition ; clustering algorithms Minimize

Rights:

I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advis...

I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. Minimize

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Title:

Stochastic Data Clustering.

Contributors:

Carl Meyer, Chair ; David Dickey, Member ; Ilse Ipsen, Member ; Ernest Stitzinger, Member

Year of Publication:

2011-05-16

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Title:

An Approach to Identify the Number of Clusters

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In this technological age, vast amounts of data are generated. Various statistical methods are used to find patterns in data, including clustering. Many common methods for cluster analysis, such as k-means and Nonnegative Matrix Factorization, require input of the number of clusters in the data. However, usually that number is unknown. There exi...

In this technological age, vast amounts of data are generated. Various statistical methods are used to find patterns in data, including clustering. Many common methods for cluster analysis, such as k-means and Nonnegative Matrix Factorization, require input of the number of clusters in the data. However, usually that number is unknown. There exists a method that uses eigenvalues to compute the number of clusters, but sometimes it underestimates that number. In this paper, we propose a complementary method to identify the number of clusters. This method is used to analyze three data sets and gives fairly accurate estimates of the number of clusters. 1 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-17

Source:

http://meyer.math.ncsu.edu/Meyer/PS_Files/NumberOfClusters.pdf

Document Type:

text

Language:

en

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