文件名称:gcforest
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Linux] [SHELL] [源码]
- 上传时间：
- 2017-06-01
- 文件大小：
- 100kb
- 下载次数：
- 0次
- 提 供 者：
- 沙**
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
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
(系统自动生成,下载前可以参看下载内容)
下载文件列表
datasets
........\gtzan
........\.....\get_data.sh
........\.....\splits
........\.....\......\blues.train
........\.....\......\blues.trainval
........\.....\......\blues.val
........\.....\......\classical.train
........\.....\......\classical.trainval
........\.....\......\classical.val
........\.....\......\country.train
........\.....\......\country.trainval
........\.....\......\country.val
........\.....\......\disco.train
........\.....\......\disco.trainval
........\.....\......\disco.val
........\.....\......\genre.train
........\.....\......\genre.trainval
........\.....\......\genre.val
........\.....\......\genres.trainval
........\.....\......\hiphop.train
........\.....\......\hiphop.trainval
........\.....\......\hiphop.val
........\.....\......\jazz.train
........\.....\......\jazz.trainval
........\.....\......\jazz.val
........\.....\......\metal.train
........\.....\......\metal.trainval
........\.....\......\metal.val
........\.....\......\pop.train
........\.....\......\pop.trainval
........\.....\......\pop.val
........\.....\......\reggae.train
........\.....\......\reggae.trainval
........\.....\......\reggae.val
........\.....\......\rock.train
........\.....\......\rock.trainval
........\.....\......\rock.val
........\uci_adult
........\.........\features
........\.........\get_data.sh
........\uci_letter
........\..........\get_data.sh
........\uci_semg
........\........\get_data.sh
........\uci_yeast
........\.........\get_data.sh
........\.........\yeast.label
lib
...\gcforest
...\........\cascade
...\........\.......\cascade_classifier.py
...\........\.......\__init__.py
...\........\datasets
...\........\........\cifar10.py
...\........\........\ds_base.py
...\........\........\ds_pickle.py
...\........\........\ds_pickle2.py
...\........\........\gtzan.py
...\........\........\imdb.py
...\........\........\mnist.py
...\........\........\olivetti_face.py
...\........\........\uci_adult.py
...\........\........\uci_letter.py
...\........\........\uci_semg.py
...\........\........\uci_yeast.py
...\........\........\__init__.py
...\........\data_cache.py
...\........\estimators
...\........\..........\base_estimator.py
...\........\..........\est_utils.py
...\........\..........\kfold_wrapper.py
...\........\..........\sklearn_estimators.py
...\........\..........\__init__.py
...\........\exp_utils.py
...\........\fgnet.py
...\........\layers
...\........\......\base_layer.py
...\........\......\fg_concat_layer.py
...\........\......\fg_pool_layer.py
...\........\......\fg_win_layer.py
...\........\......\__init__.py
...\........\utils
...\........\.....\audio_utils.py
...\........\.....\cache_utils.py
...\........\.....\config_utils.py
...\........\.....\debug_utils.py
...\........\.....\log_utils.py
...\........\.....\metrics.py
...\........\.....\win_utils.py
...\........\.....\__init__.py
...\........\__init__.py
models
......\cifar10
......\.......\gcforest
......\.......\........\fg-tree500-depth100-3folds-ca.json
......\.......\........\fg-tree500-depth100-3folds.json
......\gtzan
......\.....\gcforest
......\.....\........\ca-tree500-n4x2-3folds.json