文件名称:adaboost-and-rbf
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2015-10-15
- 文件大小：
- 264kb
- 下载次数：
- 0次
- 提 供 者：
- 刘**
- 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
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-Application of random forest algorithm in image classification and regression, better features by combining neural networks data processing
(系统自动生成,下载前可以参看下载内容)
下载文件列表
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
................\......\..........._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
......