Solver_NU

Full name Microsoft.VisualBasic.MachineLearning.SVM.Solver_NU Assembly Microsoft.VisualBasic.MachineLearning Members 2

Solver for nu-svm classification and regression additional constraint: e^T \alpha = constant

01 Syntax

Microsoft.VisualBasic.MachineLearning.SVM.Solver_NU

02 Methods

Name	Overloads	Summary
Solve	1	Solve the nu-svm quadratic programming sub-problem, the additional constraint e^T * alpha = constant is handled by this solver.
select_working_set	1	return 1 if already optimal, return 0 otherwise

03 Members

method Solve #

Solve(Int32, IQMatrix, Double(), SByte(), Double(), Double, Double, Double, SolutionInfo, Boolean)

Solve the nu-svm quadratic programming sub-problem, the additional constraint e^T * alpha = constant is handled by this solver.

Parameters

Name	Type	Description
`l`	`Int32`	The number of the variables (the training samples).
`Q`	`IQMatrix`	The kernel matrix of the current svm formulation.
`p`	`Double()`	The linear term of the objective function.
`y`	`SByte()`	The signed label (+1/-1) of each variable.
`alpha`	`Double()`	The lagrange multiplier of each variable, the solution will be written into this array.
`Cp`	`Double`	The upper bound of the lagrange multiplier of the positive side variables.
`Cn`	`Double`	The upper bound of the lagrange multiplier of the negative side variables.
`eps`	`Double`	The stopping tolerance of the iteration.
`si`	SolutionInfo	The SolutionInfo object which will be filled with the solution information.
`shrinking`	`Boolean`	Whether the shrinking heuristic should be applied during the iteration?

method select_working_set #

select_working_set(Int32())

return 1 if already optimal, return 0 otherwise

Parameters

Name	Type	Description
`working_set`	`Int32()`	The output array which will receive the indices of the two selected variables, its length should be 2 or more.

Returns

1 when the current solution is already optimal, otherwise 0.