Skip to main content


eCommons@Cornell

eCommons@Cornell >
College of Engineering >
Computer Science >
Computer Science Technical Reports >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/5925
Title: The Weakening of Taxonomic Inferences by Homological Errors
Authors: Jackson, D.M.
White, L.J.
Keywords: computer science
technical report
Issue Date: Jul-1970
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR70-66
Abstract: In the past decade there has been a growing concern in devising classification algorithms which are applicable to large bodies of data. Such algorithms are characterized necessarily by a sacrifice of statistical sophistication for a gain in computational simplicity. Accordingly, inferences drawn from taxonomic studies in which these algorithms have been employed may be affected by accidental and poorly understood features of such algorithms. An error analytic technique is presented which reduces this possibility. It is applicable to many of the classification algorithms currently in use. The combinatorial problems encountered in the error analysis are discussed and a computationally viable method for their solution is formulated. The technique is illustrated by an experiment with a small set of data.
URI: http://hdl.handle.net/1813/5925
Appears in Collections:Computer Science Technical Reports

Files in This Item:

File Description SizeFormat
70-66.pdf2.95 MBAdobe PDFView/Open
70-66.ps1.06 MBPostscriptView/Open

Refworks Export

Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2014 Cornell University Library Contact Us