<p>One statistical method of estimating an underlying distribution from a set of samples is the non-parametric kernel method. This method can be used in classification toestimate the underlying distributions of the various classes. Since it can be shown that there is no perfect shape to a kernel function used to estimate an underlyingdistribution, several functions have been proposed and none is superior in all cases. This thesis demonstrates that a function can be created that adapts its shape to fitthe properties of the underlying data set. The function adapts its shape based on a pair of parameters and the algorithm uses a hill-climbing algorithm to determine thebest pair of parameters to use for the data set. This method gets consistently better accuracy than existing non-parametric kernel methods and comparable accuracy toother classification techniques. <p>In addition, this method can estimate information about the underlying characteristics of the data, based on the optimal parameters to thekernel function. This information includes the complexity of the underlying concept function and the general noise level in the measured attributes of the examples. <P>
| Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-20000328-123728 |
| Date | 28 March 2000 |
| Creators | Kaszycki, Gregory John |
| Contributors | Dennis Bahler, Matthias Stallmann, James Lester, David Dickey |
| Publisher | NCSU |
| Source Sets | North Carolina State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://www.lib.ncsu.edu/theses/available/etd-20000328-123728 |
| Rights | unrestricted, 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, dissertation, 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. |
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