Ayrıntılı Bilimsel Program ve Bildiri Özetleri - YAEM2010

YAEM 2010
YÖNEYLEM ARAÞTIRMASI VE ENDÜSTRÝ MÜHENDÝSLÝGI 30. ULUSAL KONGRESÝ
Clusters, today, became one of the popular approaches of the regional
development. EU initiated specific support programs for cluster
development projects; besides many countries use cluster related
development programs in order to support their industrial and service
sectors.
Identification of clusters is one of the important problems that cluster
developers face. Membership, linkages and boundary problems are
some areas that need to be worked on about clusters. Since there is
not analytically developed and usable model for cluster identification,
cluster developers use their case-specific models generally. Therefore,
a quantitative cluster identification model will be very useful for the
common use of cluster developers. If such a model can be developed,
comparisons of clusters will be possible as well.
The main aim of this study is to develop a quantitative model to
identify clusters. While doing this, the research builds on its approach
on Porter's value system model, uses tools of graph theory and social
network theory.
Relaxing Support Vectors for Classification
1 2 3
Onur Þeref , W. Art Chaovalitwongse , J. Paul Brooks
1 Virginia Tech, Blacksburg, VA, USA
2 Rutgers University, Piscataway, NJ, USA
3 Virginia Commonwealth University, Richmond, VA, USA
Support Vector Machine (SVM) classifiers have been developed
substantially within the last decade and used extensively in a wide
range of application areas. SVM classifiers are based on a convex
quadratic optimization model with linear constraints, whose dual
formulation allows nonlinear mapping through kernel functions for
nonlinear classification. In this talk we introduce a modification of the
SVM formulation in order to relax the support vectors that influence
the classification boundary. The relaxed SVM formulation provides
better classification results and faster implementations. We develop
the dual relaxed SVM formulation for nonlinear classification and show
how the dual formulation can be used in a minimal sequential
optimization framework. We present error bounds for the relaxed SVM
formulation regarding stability and show that the relaxed classifier is
universally consistent.
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