svmRegression
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svmRegression
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The svmRegression function returns the Number Vector containing the trained
weights for the support vector machine regression on the specified training points.
The argument {X} contains the N x M independent variables in the form of a
number vector array: The argument {Y} contains the N dependent variables in the form of a number vector: The return value is a Structure {S} containing the following elements: The return element {Weights} contains the N trained weights for the support
vector machine dual form regression model in the form of a number vector: For a more thorough discussion of the complex subject of support vector machine
regression, please refer to "An Introduction to Support Vector Machines and other
kernel-based learning methods", by Nello Christianini and John Shawe-Taylor,
Cambridge University Press, 2000. Usage The svmRegression function can be used to perform linear and non-linear dual
form regressions on small, mid, and large scale training data. Training data up to
50,000 x 100 can be regressed, in reasonable time, on medium speed laptop computers.
A wide range of built-in support vector machine kernels are available plus user
defined kernel Lambdas are also readily accepted, making the svmRegression function
useful across a wide range of applications.
x11
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x2M
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xN1
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xNM
y1
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(svmRegression X Y kernel ETollerance maxError MaxGenerations maxSVSize printSW) The Structure {S} with elements: Error, Weights, Generations, Ey, and Py.
Expression:
Arguments
Name
Type
Description Argument: X Integer
The N x M Number Vector array of independent training points Argument: Y Integer
The N Number Vector of dependent training points Argument: kernel Symbol
The two argument support vector machine kernel function to be used
in the regression. Argument: ETollerance Real
The error tolerance limit as a percent of dependent value. Argument: maxError Real
The maximum error rate at which training may stop. Argument: MaxGenerations Integer
The maximum number of training generations at which training must stop. Argument: MaxSVSize Integer
The maximum number of support vectors attempted during initialization. Argument: printSW Symbol
The verbose mode display switch for testing purposes.
Returns:
Here are a number of links to Lambda coding examples which contain this instruction in various use cases.
Here are the links to the data types of the function arguments.
| Vector | Structure | Integer | NumVector |
| Symbol | Real |
Here are also a number of links to functions having arguments with any of these data types.
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