<div class="description">
<dl><dd>"June 1998."</dd>
<dd>Thesis advisor(s): Monique P. Farques, Ralph D. Hippenstiel</dd>
<dd>Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1998</dd>
<dd>Includes bibliographical references (p. 161-163)</dd>
<dd>This thesis investigates the application of wavelet decompositions to classification applications. Two feature extraction tools are considered: Local Discriminant Bases scheme (LDB) and Power method. Several dimension reduction schemes including a newly proposed one called the Mean Separator neural network (MS NN) are discussed. Two types of classifiers are investigated and compared: Classification Trees (CT) and Back-propagation neural network (BP NN). Classification experiments conducted on synthetic and real-world underwater signals show that: (1) the Power feature extraction method is more robust to time synchronization issues than the LDB scheme is; (2) the MS NN scheme is a successful dimension reduction scheme that may be used with both LDB and Power feature extraction methods; and (3) the BP NN is a more powerful classifier than CT as it has fewer constraints than CT in partitioning the feature input space</dd>
<dd>Mode of access: World Wide Web</dd>
<dd>System requirements: Adobe Acrobat reader</dd>
<dd>Turkish Navy author</dd>
<dd>dk/dk cc:9116 9/2/98</dd></dl><br>Subjects:</div>
<div class="fn value">
Duzenli, Ozhan.</div>
Internet Archive identifier