Publication

Numerical Experiments with Support Vector Machines

1999
Reports

Abstract

The report presents a series of numerical experiments concerning application of Support Vector Machines for the two class spatial data classification. The main attention is paid to the variability of the results by changing hyperparameters: bandwidth of the radial basis function kernel and C parameter. Training error, testing error and number of support vectors are plotted against hyperparameters. Number of support vectors is minimal at the optimal solution. Two real case studies are considered: Cd contamination in the Leman Lake, Briansk region radionuclides soil contamination. Structural analysis (variography) is used for the description of the spatial patterns obtained and to monitor the performance of SVM.

Official source

https://infoscience.epfl.ch/entities/publication/0bd04d3c-5cef-477a-8e2c-40afd6115d69

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1999
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0bd04d3c-5cef-477a-8e2c-40afd6115d69

About this result

Related concepts (25)

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Kernel method

In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets.

Structural analysis

Structural analysis is a branch of solid mechanics which uses simplified models for solids like bars, beams and shells for engineering decision making. Its main objective is to determine the effect of loads on the physical structures and their components. In contrast to theory of elasticity, the models used in structure analysis are often differential equations in one spatial variable. Structures subject to this type of analysis include all that must withstand loads, such as buildings, bridges, aircraft and ships.

Related publications (5)

Numerical Experiments with Support Vector Machines

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