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Evolving an Automatic Defect Classification Tool Assaf Glazer1,2 and Moshe Sipper1 1 Dept. of Computer Science, Ben-Gurion University,
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Document Date: 2008-05-25 07:24:28


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City

Beer-Sheva / Matlab / /

Company

Oxford University Press / Applied Materials Inc. / Neural Networks / Radial Basis Function Neural Networks / /

Country

Israel / /

Facility

Ben-Gurion University / /

IndustryTerm

k-means algorithm / data mining applications / automatic classification tool / heavy random search model / soft computing / genetic algorithm / optimization algorithm / classification processing time / heavy random search / well-developed technology / trivial solution / genetic algorithms / lithographic printing / evolutionary classification tool / image classification tool / feed-forward network / intricate algorithms / reasonable solution / search space / basis function networks / manufacturing problems / classification tool / classified product / /

OperatingSystem

XP / /

Organization

Israeli Ministry of Industry / Trade / and Employment / Ben-Gurion University / William Frankel Center for Computer Science / Oxford University / /

Person

David Sanchez / Wai Cho / Wai Mak / Choate / /

Position

defect controller / basic model / showing almost identical behavior / enhanced evolutionary model for the defect classification problem / Bishop / enhanced model / showing almost identical behavior / T.D. / controller / frequent skilled Customer Engineer / /

Product

SEMVision classification tool / SEMVision classification / /

ProvinceOrState

Georgia / /

PublishedMedium

Machine Learning / /

Technology

RAM / Machine Learning algorithms / k-means algorithm / well-developed technology / ADC / semiconductors / Genetic algorithms / neural network / Research Genetic algorithms / genetic algorithm / data mining / machine learning / EM algorithm / integrated circuits / optimization algorithm / /

URL

www.moshesipper.com / /

SocialTag