文件名称:gcforest
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周志华教授深度森林算法代码,用于分类精度接近深度学习算法-Professor zhihua s deep forest algorithm code is used to classify precision approach to deep learning algorithm
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下载文件列表
datasets/
datasets/gtzan/
datasets/gtzan/get_data.sh
datasets/gtzan/splits/
datasets/gtzan/splits/blues.train
datasets/gtzan/splits/blues.trainval
datasets/gtzan/splits/blues.val
datasets/gtzan/splits/classical.train
datasets/gtzan/splits/classical.trainval
datasets/gtzan/splits/classical.val
datasets/gtzan/splits/country.train
datasets/gtzan/splits/country.trainval
datasets/gtzan/splits/country.val
datasets/gtzan/splits/disco.train
datasets/gtzan/splits/disco.trainval
datasets/gtzan/splits/disco.val
datasets/gtzan/splits/genre.train
datasets/gtzan/splits/genre.trainval
datasets/gtzan/splits/genre.val
datasets/gtzan/splits/genres.trainval
datasets/gtzan/splits/hiphop.train
datasets/gtzan/splits/hiphop.trainval
datasets/gtzan/splits/hiphop.val
datasets/gtzan/splits/jazz.train
datasets/gtzan/splits/jazz.trainval
datasets/gtzan/splits/jazz.val
datasets/gtzan/splits/metal.train
datasets/gtzan/splits/metal.trainval
datasets/gtzan/splits/metal.val
datasets/gtzan/splits/pop.train
datasets/gtzan/splits/pop.trainval
datasets/gtzan/splits/pop.val
datasets/gtzan/splits/reggae.train
datasets/gtzan/splits/reggae.trainval
datasets/gtzan/splits/reggae.val
datasets/gtzan/splits/rock.train
datasets/gtzan/splits/rock.trainval
datasets/gtzan/splits/rock.val
datasets/uci_adult/
datasets/uci_adult/features
datasets/uci_adult/get_data.sh
datasets/uci_letter/
datasets/uci_letter/get_data.sh
datasets/uci_semg/
datasets/uci_semg/get_data.sh
datasets/uci_yeast/
datasets/uci_yeast/get_data.sh
datasets/uci_yeast/yeast.label
lib/
lib/gcforest/
lib/gcforest/cascade/
lib/gcforest/cascade/cascade_classifier.py
lib/gcforest/cascade/__init__.py
lib/gcforest/datasets/
lib/gcforest/datasets/cifar10.py
lib/gcforest/datasets/ds_base.py
lib/gcforest/datasets/ds_pickle.py
lib/gcforest/datasets/ds_pickle2.py
lib/gcforest/datasets/gtzan.py
lib/gcforest/datasets/imdb.py
lib/gcforest/datasets/mnist.py
lib/gcforest/datasets/olivetti_face.py
lib/gcforest/datasets/uci_adult.py
lib/gcforest/datasets/uci_letter.py
lib/gcforest/datasets/uci_semg.py
lib/gcforest/datasets/uci_yeast.py
lib/gcforest/datasets/__init__.py
lib/gcforest/data_cache.py
lib/gcforest/estimators/
lib/gcforest/estimators/base_estimator.py
lib/gcforest/estimators/est_utils.py
lib/gcforest/estimators/kfold_wrapper.py
lib/gcforest/estimators/sklearn_estimators.py
lib/gcforest/estimators/__init__.py
lib/gcforest/exp_utils.py
lib/gcforest/fgnet.py
lib/gcforest/layers/
lib/gcforest/layers/base_layer.py
lib/gcforest/layers/fg_concat_layer.py
lib/gcforest/layers/fg_pool_layer.py
lib/gcforest/layers/fg_win_layer.py
lib/gcforest/layers/__init__.py
lib/gcforest/utils/
lib/gcforest/utils/audio_utils.py
lib/gcforest/utils/cache_utils.py
lib/gcforest/utils/config_utils.py
lib/gcforest/utils/debug_utils.py
lib/gcforest/utils/log_utils.py
lib/gcforest/utils/metrics.py
lib/gcforest/utils/win_utils.py
lib/gcforest/utils/__init__.py
lib/gcforest/__init__.py
models/
models/cifar10/
models/cifar10/gcforest/
models/cifar10/gcforest/fg-tree500-depth100-3folds-ca.json
models/cifar10/gcforest/fg-tree500-depth100-3folds.json
models/gtzan/
models/gtzan/gcforest/
models/gtzan/gcforest/ca-tree500-n4x2-3folds.json
models/gtzan/gcforest/fg-tree500-depth100-3folds-ca.json
models/gtzan/gcforest/fg-tree500-depth100-3folds.json
models/imdb/
models/imdb/gcforest/
models/imdb/gcforest/ca-tree500-n4x2-3folds.json
models/mnist/
models/mnist/gcforest/
models/mnist/gcforest/ca-tree500-n4x2-3folds.json
models/mnist/gcforest/fg-tree500-depth100-3folds-ca.json
models/mnist/gcforest/fg-tree500-depth100-3folds.json
models/uci_adult/
models/uci_adult/gcforest/
models/uci_adult/gcforest/ca-tree500-n4x2-3folds.json
models/uci_letter/
models/uci_letter/gcforest/
models/uci_letter/gcforest/ca-tree500-n4x2-3folds.json
models/uci_semg/
models/uci_semg/gcforest/
models/uci_semg/gcforest/ca-tree500-n4x2-3folds.json
models/uci_semg/gcforest/fg-tree500-depth100-3folds-ca.json
models/uci_semg/gcforest/fg-tree500-depth100-3folds.json
models/uci_yeast/
models/uci_yeast/gcforest/
models/uci_yeast/gcforest/ca-tree500-n4x2-3folds.json
README.txt
requirements.txt
tools/
tools/audio/
tools/audio/cache_feature.py
tools/train_cascade.py
tools/train_fg.py
tools/train_xgb.py
datasets/gtzan/
datasets/gtzan/get_data.sh
datasets/gtzan/splits/
datasets/gtzan/splits/blues.train
datasets/gtzan/splits/blues.trainval
datasets/gtzan/splits/blues.val
datasets/gtzan/splits/classical.train
datasets/gtzan/splits/classical.trainval
datasets/gtzan/splits/classical.val
datasets/gtzan/splits/country.train
datasets/gtzan/splits/country.trainval
datasets/gtzan/splits/country.val
datasets/gtzan/splits/disco.train
datasets/gtzan/splits/disco.trainval
datasets/gtzan/splits/disco.val
datasets/gtzan/splits/genre.train
datasets/gtzan/splits/genre.trainval
datasets/gtzan/splits/genre.val
datasets/gtzan/splits/genres.trainval
datasets/gtzan/splits/hiphop.train
datasets/gtzan/splits/hiphop.trainval
datasets/gtzan/splits/hiphop.val
datasets/gtzan/splits/jazz.train
datasets/gtzan/splits/jazz.trainval
datasets/gtzan/splits/jazz.val
datasets/gtzan/splits/metal.train
datasets/gtzan/splits/metal.trainval
datasets/gtzan/splits/metal.val
datasets/gtzan/splits/pop.train
datasets/gtzan/splits/pop.trainval
datasets/gtzan/splits/pop.val
datasets/gtzan/splits/reggae.train
datasets/gtzan/splits/reggae.trainval
datasets/gtzan/splits/reggae.val
datasets/gtzan/splits/rock.train
datasets/gtzan/splits/rock.trainval
datasets/gtzan/splits/rock.val
datasets/uci_adult/
datasets/uci_adult/features
datasets/uci_adult/get_data.sh
datasets/uci_letter/
datasets/uci_letter/get_data.sh
datasets/uci_semg/
datasets/uci_semg/get_data.sh
datasets/uci_yeast/
datasets/uci_yeast/get_data.sh
datasets/uci_yeast/yeast.label
lib/
lib/gcforest/
lib/gcforest/cascade/
lib/gcforest/cascade/cascade_classifier.py
lib/gcforest/cascade/__init__.py
lib/gcforest/datasets/
lib/gcforest/datasets/cifar10.py
lib/gcforest/datasets/ds_base.py
lib/gcforest/datasets/ds_pickle.py
lib/gcforest/datasets/ds_pickle2.py
lib/gcforest/datasets/gtzan.py
lib/gcforest/datasets/imdb.py
lib/gcforest/datasets/mnist.py
lib/gcforest/datasets/olivetti_face.py
lib/gcforest/datasets/uci_adult.py
lib/gcforest/datasets/uci_letter.py
lib/gcforest/datasets/uci_semg.py
lib/gcforest/datasets/uci_yeast.py
lib/gcforest/datasets/__init__.py
lib/gcforest/data_cache.py
lib/gcforest/estimators/
lib/gcforest/estimators/base_estimator.py
lib/gcforest/estimators/est_utils.py
lib/gcforest/estimators/kfold_wrapper.py
lib/gcforest/estimators/sklearn_estimators.py
lib/gcforest/estimators/__init__.py
lib/gcforest/exp_utils.py
lib/gcforest/fgnet.py
lib/gcforest/layers/
lib/gcforest/layers/base_layer.py
lib/gcforest/layers/fg_concat_layer.py
lib/gcforest/layers/fg_pool_layer.py
lib/gcforest/layers/fg_win_layer.py
lib/gcforest/layers/__init__.py
lib/gcforest/utils/
lib/gcforest/utils/audio_utils.py
lib/gcforest/utils/cache_utils.py
lib/gcforest/utils/config_utils.py
lib/gcforest/utils/debug_utils.py
lib/gcforest/utils/log_utils.py
lib/gcforest/utils/metrics.py
lib/gcforest/utils/win_utils.py
lib/gcforest/utils/__init__.py
lib/gcforest/__init__.py
models/
models/cifar10/
models/cifar10/gcforest/
models/cifar10/gcforest/fg-tree500-depth100-3folds-ca.json
models/cifar10/gcforest/fg-tree500-depth100-3folds.json
models/gtzan/
models/gtzan/gcforest/
models/gtzan/gcforest/ca-tree500-n4x2-3folds.json
models/gtzan/gcforest/fg-tree500-depth100-3folds-ca.json
models/gtzan/gcforest/fg-tree500-depth100-3folds.json
models/imdb/
models/imdb/gcforest/
models/imdb/gcforest/ca-tree500-n4x2-3folds.json
models/mnist/
models/mnist/gcforest/
models/mnist/gcforest/ca-tree500-n4x2-3folds.json
models/mnist/gcforest/fg-tree500-depth100-3folds-ca.json
models/mnist/gcforest/fg-tree500-depth100-3folds.json
models/uci_adult/
models/uci_adult/gcforest/
models/uci_adult/gcforest/ca-tree500-n4x2-3folds.json
models/uci_letter/
models/uci_letter/gcforest/
models/uci_letter/gcforest/ca-tree500-n4x2-3folds.json
models/uci_semg/
models/uci_semg/gcforest/
models/uci_semg/gcforest/ca-tree500-n4x2-3folds.json
models/uci_semg/gcforest/fg-tree500-depth100-3folds-ca.json
models/uci_semg/gcforest/fg-tree500-depth100-3folds.json
models/uci_yeast/
models/uci_yeast/gcforest/
models/uci_yeast/gcforest/ca-tree500-n4x2-3folds.json
README.txt
requirements.txt
tools/
tools/audio/
tools/audio/cache_feature.py
tools/train_cascade.py
tools/train_fg.py
tools/train_xgb.py
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