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Learning Support Vector Machines (SVM) Using SCILAB


Theory and Applications of Support Vector Machines (SVM) Using SCILAB

Support Vector Machines is an important topic in machine learning. The development of SVMs involved sound theory first, then implementation and experiments.

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Support Vector Machine is an important component in machine learning. With the integration with Scilab, one could focus on the learning the algorithms instead of programing.

Course Synopsis


This course serves as introduction of Support Vector Machines (SVM) and its applications using SCILAB. The course starts with the development of SVM and LibSVM. Then, we deliver the details and definition of Pattern Recognition Problems, Classification, Regression and Clustering.

Once the participants understand the concept of SVM and pattern recognition, as well as familiar with LibSVM, the course will be focusing on the engineering problems and respective solutions using LibSVM.


Course Objectives


This course is to introduce the popular state-of-art Support Vector Machines (SVM), deliver the concept of pattern recognition, i.e., classification, regression and clustering and to demonstrate the solutions of engineering problems using SVM.

Who Must Attend

Postgraduate students, Researchers, and Lecturers who need a powerful pattern recognition toolbox to complete their projects. Academician who wish to explore and integrate the newly computational intelligence techniques, i.e., Support Vector  Machines, into their respective field of research and anyone in Engineering who looks for alternative solutions for the real world engineering problems.


Basic Engineering Mathematics. Experience in SCILAB programming will be an advantage. Knowledge in Artificial Intelligence will be an advantage.

Course Outline

Introduction of Pattern Classification, Regression and Clustering

  • Theory and Development of Support Vector Machines (SVM)
  • LibSVM – A Powerful SCILAB module for SVM

Applications of Support Vector Classification (SVC)

  • Exercise of SVC on artificial data (with graphical results)
  • Power Systems – Conditional Monitoring of Transformer
  • Civil  Engineering – Occurrence of Flashover in Compartment Fire
  • Medical Diagnosis – Diagnostic of Breast Cancer

Applications of Support Vector Regression (SVR)

  • Exercise of SVR on artificial data (with graphical results)
  • Power Systems – Electrical Power Load Forecasting
  • Civil Engineering – Evacuation Times in Fire Event
  • Control Engineering – Identification and Control of Dynamic Systems

Introduction of Support Vector Clustering and exercise based on artificial data

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