Classification🔗

Gaussian Mixture Models🔗

add_class_train_data_gmmAddClassTrainDataGmm: Add training data to a Gaussian Mixture Model (GMM).

add_sample_class_gmmAddSampleClassGmm: Add a training sample to the training data of a Gaussian Mixture Model.

classify_class_gmmClassifyClassGmm: Calculate the class of a feature vector by a Gaussian Mixture Model.

clear_class_gmmClearClassGmm: Clear a Gaussian Mixture Model.

clear_samples_class_gmmClearSamplesClassGmm: Clear the training data of a Gaussian Mixture Model.

create_class_gmmCreateClassGmm: Create a Gaussian Mixture Model for classification

deserialize_class_gmmDeserializeClassGmm: Deserialize a serialized Gaussian Mixture Model.

evaluate_class_gmmEvaluateClassGmm: Evaluate a feature vector by a Gaussian Mixture Model.

get_class_train_data_gmmGetClassTrainDataGmm: Get the training data of a Gaussian Mixture Model (GMM).

get_params_class_gmmGetParamsClassGmm: Return the parameters of a Gaussian Mixture Model.

get_prep_info_class_gmmGetPrepInfoClassGmm: Compute the information content of the preprocessed feature vectors of a GMM.

get_sample_class_gmmGetSampleClassGmm: Return a training sample from the training data of a Gaussian Mixture Models (GMM).

get_sample_num_class_gmmGetSampleNumClassGmm: Return the number of training samples stored in the training data of a Gaussian Mixture Model (GMM).

read_class_gmmReadClassGmm: Read a Gaussian Mixture Model from a file.

read_samples_class_gmmReadSamplesClassGmm: Read the training data of a Gaussian Mixture Model from a file.

select_feature_set_gmmSelectFeatureSetGmm: Selects an optimal combination from a set of features to classify the provided data.

serialize_class_gmmSerializeClassGmm: Serialize a Gaussian Mixture Model (GMM).

train_class_gmmTrainClassGmm: Train a Gaussian Mixture Model.

write_class_gmmWriteClassGmm: Write a Gaussian Mixture Model to a file.

write_samples_class_gmmWriteSamplesClassGmm: Write the training data of a Gaussian Mixture Model to a file.

K-Nearest Neighbor🔗

add_class_train_data_knnAddClassTrainDataKnn: Add training data to a k-nearest neighbors (k-NN) classifier.

add_sample_class_knnAddSampleClassKnn: Add a sample to a k-nearest neighbors (k-NN) classifier.

classify_class_knnClassifyClassKnn: Search for the next neighbors for a given feature vector.

clear_class_knnClearClassKnn: Clear a k-NN classifier.

create_class_knnCreateClassKnn: Create a k-nearest neighbors (k-NN) classifier.

deserialize_class_knnDeserializeClassKnn: Deserialize a serialized k-NN classifier.

get_class_train_data_knnGetClassTrainDataKnn: Get the training data of a k-nearest neighbors (k-NN) classifier.

get_params_class_knnGetParamsClassKnn: Get parameters of a k-NN classification.

get_sample_class_knnGetSampleClassKnn: Return a training sample from the training data of a k-nearest neighbors (k-NN) classifier.

get_sample_num_class_knnGetSampleNumClassKnn: Return the number of training samples stored in the training data of a k-nearest neighbors (k-NN) classifier.

read_class_knnReadClassKnn: Read the k-NN classifier from a file.

select_feature_set_knnSelectFeatureSetKnn: Selects an optimal subset from a set of features to solve a certain classification problem.

serialize_class_knnSerializeClassKnn: Serialize a k-NN classifier.

set_params_class_knnSetParamsClassKnn: Set parameters for k-NN classification.

train_class_knnTrainClassKnn: Creates the search trees for a k-NN classifier.

write_class_knnWriteClassKnn: Save the k-NN classifier in a file.

Look-UP Table🔗

clear_class_lutClearClassLut: Clear a look-up table classifier.

create_class_lut_gmmCreateClassLutGmm: Create a look-up table using a Gaussian mixture model to classify byte images.

create_class_lut_knnCreateClassLutKnn: Create a look-up table using a k-nearest neighbors classifier (k-NN) to classify byte images.

create_class_lut_mlpCreateClassLutMlp: Create a look-up table using a multi-layer perceptron to classify byte images.

create_class_lut_svmCreateClassLutSvm: Create a look-up table using a Support-Vector-Machine to classify byte images.

Misc🔗

add_sample_class_train_dataAddSampleClassTrainData: Add a training sample to training data.

clear_class_train_dataClearClassTrainData: Clears training data for classifiers.

create_class_train_dataCreateClassTrainData: Create a handle for training data for classifiers.

deserialize_class_train_dataDeserializeClassTrainData: Deserialize serialized training data for classifiers.

get_sample_class_train_dataGetSampleClassTrainData: Return a training sample from training data.

get_sample_num_class_train_dataGetSampleNumClassTrainData: Return the number of training samples stored in the training data.

read_class_train_dataReadClassTrainData: Read the training data for classifiers from a file.

select_sub_feature_class_train_dataSelectSubFeatureClassTrainData: Select certain features from training data to create training data containing less features.

serialize_class_train_dataSerializeClassTrainData: Serialize training data for classifiers.

set_feature_lengths_class_train_dataSetFeatureLengthsClassTrainData: Define subfeatures in training data.

write_class_train_dataWriteClassTrainData: Save the training data for classifiers in a file.

Neural Nets🔗

add_class_train_data_mlpAddClassTrainDataMlp: Add training data to a multilayer perceptron (MLP).

add_sample_class_mlpAddSampleClassMlp: Add a training sample to the training data of a multilayer perceptron.

classify_class_mlpClassifyClassMlp: Calculate the class of a feature vector by a multilayer perceptron.

clear_class_mlpClearClassMlp: Clear a multilayer perceptron.

clear_samples_class_mlpClearSamplesClassMlp: Clear the training data of a multilayer perceptron.

create_class_mlpCreateClassMlp: Create a multilayer perceptron for classification or regression.

deserialize_class_mlpDeserializeClassMlp: Deserialize a serialized multilayer perceptron.

evaluate_class_mlpEvaluateClassMlp: Calculate the evaluation of a feature vector by a multilayer perceptron.

get_class_train_data_mlpGetClassTrainDataMlp: Get the training data of a multilayer perceptron (MLP).

get_params_class_mlpGetParamsClassMlp: Return the parameters of a multilayer perceptron.

get_prep_info_class_mlpGetPrepInfoClassMlp: Compute the information content of the preprocessed feature vectors of a multilayer perceptron.

get_regularization_params_class_mlpGetRegularizationParamsClassMlp: Return the regularization parameters of a multilayer perceptron.

get_rejection_params_class_mlpGetRejectionParamsClassMlp: Get the parameters of a rejection class.

get_sample_class_mlpGetSampleClassMlp: Return a training sample from the training data of a multilayer perceptron.

get_sample_num_class_mlpGetSampleNumClassMlp: Return the number of training samples stored in the training data of a multilayer perceptron.

read_class_mlpReadClassMlp: Read a multilayer perceptron from a file.

read_samples_class_mlpReadSamplesClassMlp: Read the training data of a multilayer perceptron from a file.

select_feature_set_mlpSelectFeatureSetMlp: Selects an optimal combination of features to classify the provided data.

serialize_class_mlpSerializeClassMlp: Serialize a multilayer perceptron (MLP).

set_regularization_params_class_mlpSetRegularizationParamsClassMlp: Set the regularization parameters of a multilayer perceptron.

set_rejection_params_class_mlpSetRejectionParamsClassMlp: Set the parameters of a rejection class.

train_class_mlpTrainClassMlp: Train a multilayer perceptron.

write_class_mlpWriteClassMlp: Write a multilayer perceptron to a file.

write_samples_class_mlpWriteSamplesClassMlp: Write the training data of a multilayer perceptron to a file.

Support Vector Machines🔗

add_class_train_data_svmAddClassTrainDataSvm: Add training data to a support vector machine (SVM).

add_sample_class_svmAddSampleClassSvm: Add a training sample to the training data of a support vector machine.

classify_class_svmClassifyClassSvm: Classify a feature vector by a support vector machine.

clear_class_svmClearClassSvm: Clear a support vector machine.

clear_samples_class_svmClearSamplesClassSvm: Clear the training data of a support vector machine.

create_class_svmCreateClassSvm: Create a support vector machine for pattern classification.

deserialize_class_svmDeserializeClassSvm: Deserialize a serialized support vector machine (SVM).

evaluate_class_svmEvaluateClassSvm: Evaluate a feature vector by a support vector machine.

get_class_train_data_svmGetClassTrainDataSvm: Get the training data of a support vector machine (SVM).

get_params_class_svmGetParamsClassSvm: Return the parameters of a support vector machine.

get_prep_info_class_svmGetPrepInfoClassSvm: Compute the information content of the preprocessed feature vectors of a support vector machine

get_sample_class_svmGetSampleClassSvm: Return a training sample from the training data of a support vector machine.

get_sample_num_class_svmGetSampleNumClassSvm: Return the number of training samples stored in the training data of a support vector machine.

get_support_vector_class_svmGetSupportVectorClassSvm: Return the index of a support vector from a trained support vector machine.

get_support_vector_num_class_svmGetSupportVectorNumClassSvm: Return the number of support vectors of a support vector machine.

read_class_svmReadClassSvm: Read a support vector machine from a file.

read_samples_class_svmReadSamplesClassSvm: Read the training data of a support vector machine from a file.

reduce_class_svmReduceClassSvm: Approximate a trained support vector machine by a reduced support vector machine for faster classification.

select_feature_set_svmSelectFeatureSetSvm: Selects an optimal combination of features to classify the provided data.

serialize_class_svmSerializeClassSvm: Serialize a support vector machine (SVM).

train_class_svmTrainClassSvm: Train a support vector machine.

write_class_svmWriteClassSvm: Write a support vector machine to a file.

write_samples_class_svmWriteSamplesClassSvm: Write the training data of a support vector machine to a file.