On Ranking Senators by Their Votes

Rwebangira, Mugizi

doi:10.1007/978-3-642-25781-0_39

Mugizi Rwebangira⁶

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 124))

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Abstract

The problem of ranking a set of objects given some measure of similarity is one of the most basic in machine learning. Recently Agarwal [1] proposed a method based on techniques in semi-supervised learning utilizing the graph Laplacian. In this work we consider a novel application of this technique to ranking binary choice data and apply it specifically to ranking US Senators by their ideology.

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Authors and Affiliations

Systems and Computer Science Department, Howard University, Washington, D.C., USA
Mugizi Rwebangira

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Mugizi Rwebangira
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Editors and Affiliations

College of Communication Engineering, Jilin University, Room 313, Building No.1, Changchun, Nanhu Avenue 5372, 130012, Jilin, China, People’s Republic
Zhihong Qian
Department of Electrical Engineering, The University of Mississippi, Anderson Hall 314, 38677, Mississippi, Mississippi, USA
Lei Cao
Department of Electrical and Computer Eng., Naval Postgraduate School, Rm. 452 Spanagel Bldg. 232, Dyer Road 833, 93943-5121, Monterey, California, USA
Weilian Su
Faculty of Computing, London Metropolitan University, Holloway Road 166-220, N7 8DB, London, United Kingdom
Tingkai Wang
College of Software, Changchun University of Science and Tech., Changchun, 130022, Jilin, China, People’s Republic
Huamin Yang

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Rwebangira, M. (2012). On Ranking Senators by Their Votes. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_39

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DOI: https://doi.org/10.1007/978-3-642-25781-0_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25780-3
Online ISBN: 978-3-642-25781-0
eBook Packages: EngineeringEngineering (R0)

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