SVM: Support Vector Machines
Training Initialization Dialog
Parameter Information
Classification Input
The SVM training process requires the supplied expression data and
an additional initial presumptive classification which indicates
which elements are initially presumed to have a relationship.
Two options are provided for selecting members of the initial classification.
Use SVM Classification Editor
This option causes an editor application to be launched in order to
allow a flexible tool for finding and marking elements to be positive members
of the initial classification. This classification can be save as an
SVC file for later recovery of these initial settings.
Use Classification File
This allow the loading of an initial classification from an
existing SVC file.
Kernel Matrix Construction
One can select to construct a polynomial or a radial kernal matrix.
Polynomial Kernel Function Parameters
The polynomial option is the default and three parameters are used to define
the kernel construction.
Constant
An additive constant. (c)
Coefficient
A multiplicative constant. (w)
Power
A power factor. (p)
Polynomial Kernel Function
K(i,j) = [w*(Dist(i,j)+c)]p
Radial Basis Function Parameters
The Radial Basis checkbox is used to select to use this type of Kernal generating function.
Width Factor
Radial width factor (w, see in below formula).
Radial Basis Kernel Function
K(x,y) = e( - (||x - y||^2)/(2w^2))
Training Parameters
Diagonal Factor
Constant added to the main diagonal of the kernel matrix.
Adding this factor to the main diagonal of the kernel is required to force
the matrix to be 'positive definite'. The definition of a positive definite
matrix is best reviewed in books devoted to linear algebra but this
state is achieved by selecting a constant of sufficient magnitude.
This positive definite state of the kernel matrix is required for the SVM algorithm to
yeild meaningful results. Testing values starting at 1.0 and increasing
may be required to find an appropriate value. If the value is too low all
elements will be partitioned in the negative class. For a range of values
for this factor a stable set of elements may be classified as positive.
At very high values there is a tendancy to force all positive expamples
to be in the positive class regardless of their similarity of expression.
Threshold
This value is used as a stopping criteria for the weight optimization phase of training.
Optimizing the weights produced during training is an iterative process which converges
on an optimal set of weights to separate the positive and negative examples.
This threshold dictates how stable the weights must be before the optimization process
is terminated. Selection of a threshold that is very low could cause the
optimization process to take an extremely long time and yet yeild similar results to those where
a higher threshold value was used which terminated the process earlier.
Constraints
This check box selects to apply limits to weights produced during training.
Positive Constraint
The upper limit to produced weights.
Negative Constraint
The lower limit to produced weights.