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Machine learning approach towards au...

Chen, Zhaohui.

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Machine learning approach towards automatic target recognition.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning approach towards automatic target recognition./
Author:	Chen, Zhaohui.
Description:	105 p.
Notes:	Adviser: Yu-chi Ho.
Contained By:	Dissertation Abstracts International62-10B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3028376
ISBN:	0493406530

Machine learning approach towards automatic target recognition.
Chen, Zhaohui.

Machine learning approach towards automatic target recognition. - 105 p.

Adviser: Yu-chi Ho.

Thesis (Ph.D.)--Harvard University, 2001.

The overall goal of this research project is to develop online algorithms for detecting military targets in radar return images. In this dissertation, <italic> Automatic Target Recognition</italic> is taken as a multi-stage generalized pattern recognition problem. Three problems are addressed: <italic>feature extraction, feature selection</italic> and <italic>inductive learning</italic>.

ISBN: 0493406530Subjects--Topical Terms:

626642
Computer Science.

Machine learning approach towards automatic target recognition.
LDR:02292nam 2200289 a 45 001 936296
005 20110510
008 110510s2001 eng d
020 $a 0493406530
035 $a (UnM)AAI3028376
035 $a AAI3028376
040 $a UnM $c UnM
100 1 $a Chen, Zhaohui. $3 677431
245 1 0 $a Machine learning approach towards automatic target recognition.
300 $a 105 p.
500 $a Adviser: Yu-chi Ho.
500 $a Source: Dissertation Abstracts International, Volume: 62-10, Section: B, page: 4623.
502 $a Thesis (Ph.D.)--Harvard University, 2001.
520 $a The overall goal of this research project is to develop online algorithms for detecting military targets in radar return images. In this dissertation, <italic> Automatic Target Recognition</italic> is taken as a multi-stage generalized pattern recognition problem. Three problems are addressed: <italic>feature extraction, feature selection</italic> and <italic>inductive learning</italic>.
520 $a Four mathematical transforms are utilized to extract features from raw image chips. They are <italic>Fourier Transform, Principle Component Analysis, Singular Value Decomposition</italic> and <italic>Radon Transform</italic>. Feature selection is carried out to identify important features and to reduce the dimensionality of feature space. In this work, feature selection is considered as a stochastic combinatorial optimization problem. The estimated error rate of a naive Bayesian classifier is used as performance metric to evaluate the goodness of a given subset while <italic>forward multi-selection</italic> algorithm is proposed to improve the efficiency of searching in large search space. For inductive learning, a decision fusion scheme is proposed to improve the predictive accuracy of weak learners. Different subsets of features are utilized to train weak learners and several combining algorithms are compared here. Finally, all the techniques developed are put together to solve the Automatic Target Recognition problem.
590 $a School code: 0084.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0984
710 2 0 $a Harvard University. $3 528741
773 0 $t Dissertation Abstracts International $g 62-10B.
790 $a 0084
790 1 0 $a Ho, Yu-chi, $e advisor
791 $a Ph.D.
792 $a 2001
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3028376