CreateBoostClassifier

Create a Boost classifier. The initial reference count is set to one if a new model is created and kept, otherwise. The Boost classifier is only applicable to binary classification problems. It learns to distinguish between samples labelled with two user-defined class labels by incrementally adding weak classifiers to improve the classification results. Models of this type neither support on-line training (sample by sample) nor retraining.

Syntax

Definition:

HRESULT CreateBoostClassifier(
    HRESULT                  hrPrev,
    ITcVnMlModel*&           ipMlModel,
    ETcVnBoostClassifierType eType = BCT_REAL,
    ULONG                    nMaxDepth = 1,
    ULONG                    nMinSamples = 10,
    ULONG                    nWeakClassifiers = 100,
    double                   fWeightTrimRate = 0.95,
    ITcVnContainer*          ipClassPriors = nullptr
)

Parameters

Name	Type	Default	Description
hrPrev	HRESULT		HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)
ipMlModel	ITcVnMlModel*&		Returns the created model (Non-zero interface pointers are reused.)
eType	ETcVnBoostClassifierType	BCT_REAL	Learning algorithm type (default: TCVN_BCT_REAL)
nMaxDepth	ULONG	1	Maximum tree depth (default: 1)
nMinSamples	ULONG	10	Minimum number of samples within a node required for splitting (default: 10)
nWeakClassifiers	ULONG	100	Number of weak classifiers (default: 100)
fWeightTrimRate	double	0.95	Weight threshold used during training (off: 0; default: 0.95).
ipClassPriors	ITcVnContainer*	nullptr	Class priors (ContainerType_Vector_REAL or ContainerType_Vector_LREAL; only for classifiers; optional, set to 0 if not required or not allowed; default: 0)

Return value

HRESULT

Required License

TC3 Vision Machine Learning

System Requirements

Development environment	Target platform	PLC libraries to include
TwinCAT V3.1.4024.54 or later	PC or CX (x64) with PL50, e.g. Intel 4-core Atom CPU	Tc3_Vision