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AISS
2010
147views more  AISS 2010»
14 years 7 months ago
Correlating Lattice Constant of Cubic Perovskites to Atomic Parameters Using Support Vector Regression
Recently, due to problems arising from lattice mismatch in thin film growth in semiconductor manufacturing industry, researchers have shown great interest in modeling the physioch...
Abdul Majid, Yeon Soo Lee
ICPR
2004
IEEE
15 years 11 months ago
Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video
This paper presents a novel face detection method in video by using Discriminating Feature Analysis (DFA) and Support Vector Machine (SVM). Our method first incorporates temporal ...
Chengjun Liu, Peichung Shih
OSDI
2008
ACM
15 years 10 months ago
Predicting Computer System Failures Using Support Vector Machines
Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, applications, and services can be scheduled around predicted failure...
Errin W. Fulp, Glenn A. Fink, Jereme N. Haack
COLT
1999
Springer
15 years 2 months ago
Covering Numbers for Support Vector Machines
—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on th...
Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Ro...
ESANN
2006
14 years 11 months ago
OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method
Abstract. We present the OnlineDoubleMaxMinOver approach to obtain the Support Vectors in two class classification problems. With its linear time complexity and linear convergence ...
Daniel Schneegaß, Thomas Martinetz, Michael ...