Machine Learning
This group contains functions for creating, training and applying Machine Learning models as well as functions for feature pre-processing. A brief overview of the possible applications of Machine Learning and further descriptions can be found under the software concept.
Functions for creating a model:
- CreateBoostClassifier
- CreateKmppModel
- CreateKnnModel
- CreateLbgModel
- CreateLdaTransform
- CreateLdaTransformViaComponentNum
- CreateNbcModel
- CreatePcaTransform
- CreatePcaTransformViaComponentNum
- CreatePcaTransformViaVariance
- CreateRTreesModel
- CreateStaModel
- CreateSvmModel
- CreateSvmSgdClassifier
Functions for training a model:
- TrainBatch
- TrainBatchClusters
- TrainSample
- TrainSampleClass
- TrainSampleCluster
- TrainSampleScalar
- TrainSampleVector
Functions for obtaining information on the cluster models:
Functions for applying models to a sample:
Functions for applying models to a batch:
Functions for applying models for feature transformation / dimension reduction:
Functions for feature normalization:
Further Information
- CreateBoostClassifier
- CreateKmppModel
- CreateKnnModel
- CreateLbgModel
- CreateLdaTransform
- CreateLdaTransformViaComponentNum
- CreateNbcModel
- CreatePcaTransform
- CreatePcaTransformViaComponentNum
- CreatePcaTransformViaVariance
- CreateRTreesModel
- CreateStaModel
- CreateSvmModel
- CreateSvmSgdClassifier
- FeatureScaling
- FeatureTransform
- GetBatchClusters
- GetBatchNovelties
- GetClusterCenter
- GetClusterNum
- GetFeatureScales
- GetSampleCluster
- GetSampleNovelty
- InverseFeatureScaling
- InverseFeatureScaling (float)
- InverseFeatureTransform
- PredictBatch
- PredictSampleClass
- PredictSampleScalar
- PredictSampleVector
- TrainBatch
- TrainBatchClusters
- TrainSample
- TrainSampleClass
- TrainSampleCluster
- TrainSampleScalar
- TrainSampleVector