中文说明：
随机森林算法在图像特征分类回归中的应用，通过结合神经网络进行更好的特征数据处理。
English Description：
Random forest algorithm in the application of image feature classification regression, and by combining the characteristics of the neural network for better data processing.
Random forest algorithm in image feature classification the application of the regression
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文件大小： 264KB
中文说明：
随机森林算法在图像特征分类回归中的应用，通过结合神经网络进行更好的特征数据处理。
English Description：
Random forest algorithm in the application of image feature classification regression, and by combining the characteristics of the neural network for better data processing.adaboost and rbf
................\abr_v1
................\......\@adabooster
................\......\...........\adabooster.m
................\......\...........\calc_output.m
................\......\...........\calc_output_step.m
................\......\...........\calc_output_steps.m
................\......\...........\comp_distr.m
................\......\...........\comp_weight.m
................\......\...........\CVS
................\......\...........\...\Entries
................\......\...........\...\Repository
................\......\...........\...\Root
................\......\...........\display.m
................\......\...........\do_learn.m
................\......\...........\finish_learn.m
................\......\...........\get_class_errors_step.m
................\......\...........\get_last_distr.m
................\......\...........\get_use_sign_output.m
................\......\...........\init_learn.m
................\......\...........\private
................\......\...........\.......\CVS
................\......\...........\.......\...\Entries
................\......\...........\.......\...\Repository
................\......\...........\.......\...\Root
................\......\...........\.......\equal.m
................\......\...........\.......\erfunc.m
................\......\...........\.......\fmin.m
................\......\...........\.......\sigmoid.m
................\......\...........\report.m
................\......\...........\set_last_distr.m
................\......\...........\set_use_sign_output.m
................\......\...........\subsasgn.m
................\......\...........\subsref.m
................\......\@adabooster_regul
................\......\.................\adabooster_regul.m
................\......\.................\boost_func.m
................\......\.................\boost_func_der.m
................\......\.................\comp_distr.m
................\......\.................\comp_weight.m
................\......\.................\CVS
................\......\.................\...\Entries
................\......\.................\...\Repository
................\......\.................\...\Root
................\......\.................\display.m
................\......\.................\do_learn.m
................\......\.................\get_fin_hyp.m
................\......\.................\get_infl.m
................\......\.................\get_phi.m
................\......\.................\get_vi.m
................\......\.................\private
................\......\.................\.......\CVS
................\......\.................\.......\...\Entries
................\......\.................\.......\...\Repository
................\......\.................\.......\...\Root
................\......\.................\.......\equal.m
................\......\.................\.......\erfunc.m
................\......\.................\.......\fmin.m
................\......\.................\.......\sigmoid.m
................\......\.................\set_fin_hyp.m
................\......\.................\set_infl.m
................\......\.................\subsasgn.m
................\......\.................\subsref.m
................\......\@booster_base
................\......\.............\booster_base.m
................\......\.............\CVS
................\......\.............\...\Entries
................\......\.............\...\Repository
................\......\.............\...\Root
................\......\.............\display.m
................\......\.............\get_boosted_learner.m
................\......\.............\get_boost_steps.m
................\......\.............\get_param.m
................\......\.............\get_proto.m
................\......\.............\get_vote_weight.m
................\......\.............\get_vote_weights.m
................\......\.............\set_boosted_learner.m
................\......\.............\set_boost_steps.m
................\......\.............\set_param.m
................\......\.............\set_proto.m
................\......\.............\set_vote_weights.m
................\......\.............\subsasgn.m
................\......\.............\subsref.m
................\......\.............\train_weak.m
................\......\@data
................\......\.....\check_std.m
................\......\.....\consistent.m
................\......\.....\data.asv
................\......\.....\data.m
................\......\.....\display.m
................\......\.....\get_idim.m
................\......\.....\get_name.m
................\......\.....\get_nsname.m
................\......\.....\get_odim.m
................\......\.....\get_sname.m
................\......\.....\get_test.m
................\......\.....\get_test_size.m
................\......\.....\get_train.m
................\......\.....\get_train_size.m
................\......\.....\get_val.m