機械学習
Dlibには、幅広い機械学習アルゴリズムが含まれています。すべてが高度にモジュール化され、実行が速く、そして清潔で最新のC ++ APIを介して使用するのが簡単になるように設計されています。これは、ロボット工学、組み込み機器、携帯電話、そして大規模な高性能コンピューティング環境など、幅広いアプリケーションで使用されています。あなたの研究でdlibを使っているならば、引用してください:
Davis E. King. DLIB-ML:機械学習ツールキット。 ournal of Machine Learning Research, 2009
@Article{dlib09, author = {Davis E. King}, title = {Dlib-ml: A Machine Learning Toolkit}, journal = {Journal of Machine Learning Research}, year = {2009}, volume = {10}, pages = {1755-1758}, }
Primary Algorithms
Binary Classification
- auto_train_rbf_classifier
- rvm_trainer
- svm_c_ekm_trainer
- svm_c_linear_dcd_trainer
- svm_c_linear_trainer
- svm_c_trainer
- svm_nu_trainer
- svm_pegasos
- train_probabilistic_decision_function
Multiclass Classification
Regression
- krls
- krr_trainer
- mlp
- random_forest_regression_trainer
- rbf_network_trainer
- rls
- rr_trainer
- rvm_regression_trainer
- svr_linear_trainer
- svr_trainer
Structured Prediction
- Core Tools
- Problem Instances
- shape_predictor_trainer
- structural_assignment_trainer
- structural_graph_labeling_trainer
- structural_object_detection_trainer
- structural_sequence_labeling_trainer
- structural_sequence_segmentation_trainer
- structural_track_association_trainer
- svm_rank_trainer
Deep Learning
Clustering
- bottom_up_cluster
- chinese_whispers
- find_clusters_using_angular_kmeans
- find_clusters_using_kmeans
- kkmeans
- modularity
- nearest_center
- newman_cluster
- pick_initial_centers
- segment_number_line
- spectral_cluster
Unsupervised
- cca
- empirical_kernel_map
- kcentroid
- linearly_independent_subset_finder
- sammon_projection
- svm_one_class_trainer
- vector_normalizer
- vector_normalizer_pca
Semi-Supervised/Metric Learning
Reinforcement Learning
Feature Selection
Other Tools
Validation
- average_precision
- compute_roc_curve
- cross_validate_assignment_trainer
- cross_validate_graph_labeling_trainer
- cross_validate_multiclass_trainer
- cross_validate_object_detection_trainer
- cross_validate_ranking_trainer
- cross_validate_regression_trainer
- cross_validate_sequence_labeler
- cross_validate_sequence_segmenter
- cross_validate_track_association_trainer
- cross_validate_trainer
- cross_validate_trainer_threaded
- equal_error_rate
- test_assignment_function
- test_binary_decision_function
- test_graph_labeling_function
- test_multiclass_decision_function
- test_object_detection_function
- test_ranking_function
- test_regression_function
- test_sequence_labeler
- test_sequence_segmenter
- test_shape_predictor
- test_track_association_function
Trainer Adapters
- batch
- batch_cached
- null_trainer
- probabilistic
- reduced
- reduced2
- roc_c1_trainer
- roc_c2_trainer
- verbose_batch
- verbose_batch_cached
Kernels
- histogram_intersection_kernel
- linear_kernel
- offset_kernel
- polynomial_kernel
- radial_basis_kernel
- sigmoid_kernel
- sparse_histogram_intersection_kernel
- sparse_linear_kernel
- sparse_polynomial_kernel
- sparse_radial_basis_kernel
- sparse_sigmoid_kernel
Function Objects
- assignment_function
- decision_function
- distance_function
- graph_labeler
- multiclass_linear_decision_function
- normalized_function
- one_vs_all_decision_function
- one_vs_one_decision_function
- policy
- probabilistic_decision_function
- probabilistic_function
- projection_function
- random_forest_regression_function
- sequence_labeler
- sequence_segmenter
- track_association_function
Data IO
- fix_nonzero_indexing
- load_image_dataset
- load_image_dataset_metadata
- load_libsvm_formatted_data
- make_bounding_box_regression_training_data
- save_image_dataset_metadata
- save_libsvm_formatted_data
Miscellaneous
- approximate_distance_function
- compute_mean_squared_distance
- count_ranking_inversions
- fill_lisf
- find_gamma_with_big_centroid_gap
- is_assignment_problem
- is_binary_classification_problem
- is_forced_assignment_problem
- is_graph_labeling_problem
- is_learning_problem
- is_ranking_problem
- is_sequence_labeling_problem
- is_sequence_segmentation_problem
- is_track_association_problem
- kernel_matrix
- learn_platt_scaling
- process_sample
- randomize_samples
- ranking_pair
- select_all_distinct_labels
- simplify_linear_decision_function