classdef?Adaboost???%Adaboost
??
??properties
????
????k_max;
????k_next;
????k_cur;
????part_trains;
????base_learner;
????learners;
????learner_weights;
????early_terminate;
????d_weights;
????d_size;
????
??end
??
??methods
????
????function?a?=?Adaboost（k_max?base_learner?early_terminate）
??????%k-max:?Size?of?the?model?integer?>=?1
??????%base_learner:?Model?constructor?function?e.g.?@（）CART（）
??????%The?model?must?be?an?object?with?methods?a.train（inputs?outputs）
??????%and?outputs?=?a.test（inputs）.
??????%early_terminate:?Optional?detemines?whether?to?terminate?if?base
??????%learner?error?is?greater?than?0.5?（default?is?to?continue?and?the
??????%base?learner?receives?a?negative?weight）.
??????if?（nargin?????????early_terminate?=?false;
??????end
??????a.k_max?=?k_max;
??????a.part_trains?=?a.k_max;
??????a.k_next?=?a.k_max;
??????a.learners?=?cell（a.k_max?1）;
??????a.learner_weights?=?zeros（a.k_max?1）;
??????a.base_learner?=?base_learner;
??????a.early_terminate?=?early_terminate;
??????a.k_cur?=?0;
????end
????
????function?part_train（a?inputs?outputs?k_next）
??????
??????a.k_next?=?k_next;
??????a.train（inputs?outputs）;
??????a.k_next?=?a.k_max;
??????
????end
????
????function?train（a?inputs?outputs）
??????
??????if?（a.k_cur?==?0）????????
????????a.d_size?=?size（inputs?1）;
????????a.d_weights?=?ones（a.d_size?1）?./?a.d_size;
??????end
????????????
??????while?（a.k_cur?????????
????????a.k_cur?=?a.k_cur?+?1;
????????%Create?an?NxN?square?of?cumulatively?summed?weights?（i.e.?N
????????%rows?containing?the?weight?cumsum）.?Create?an?NxN?square?of?random
????????%numbers?（N?columns?containing?the?same?N?random?numbers）.?Compare
????????%them?and?sum?the?rows.?The?number?of?weights?that?are?less?than
????????%each?random?number?indicates?the?index?sampled?by?that?row.
????????indices?=?sum（repmat（cumsum（a.d_weights）‘?a.d_size?1）?<=?...
??????????repmat（rand（a.d_size?1）?1?a.d_size）?2）?+?1;
????????a.learners{a.k_cur}?=?a.base_learner（）;
????????a.learners{a.k_cur}.train（inputs（indices?:）?outputs（indices））;
????????
????????predictions?=?a.learners{a.k_cur}.test（inputs）?==?outputs;
????????weighted_error?=?sum（~predictions?.*?a.d_weights）;
????????
????????if?（a.early_terminate?&&?weighted_error?>?0.5）
??????????a.k_max?=?a.k_cur?-?1;
??????????a.k_cur?=?a.k_cur?-?1;
??????????a.learner_weights?=?a.learner_weights（1:a.k_max）;
??????????break;
????????end
????????
????????weighted_error?=?min（max（weighted_error?0.01）?0.99）;
????????a.learner_weights（a.k_cur）?=?0.5?*?log（（1?-?weighted_error）?/?weighted_error）;
????????
????????a.d_weights?=?feval（@（x）x./sum（x）?a.d_weights?.*?...
??????????exp（-a.learner_weights（a.k_cur）?*?ssign（predictions）））;
????????
??????end
??????
????end
????
????function?outputs?=?test（a?inputs）
??????
??????outputs?=?ssign（a.margins（inputs））;
??????
????end
????
????function?margins?=?margins（a?inputs）
?????
??????margins?=?cell2mat（arrayfun（@（x）a.learners{x}.test（i

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件?????????323??2010-11-02?15:09??boosting_demo\ssign.m
?????文件????????1189??2010-11-02?15:10??boosting_demo\NeuralNetwork.m
?????文件????????2619??2010-11-02?15:06??boosting_demo\OnlineNaiveBayes.m
?????文件???????18577??2010-11-02?15:02??boosting_demo\boosting_demo.m
?????文件?????????692??2010-11-02?15:10??boosting_demo\SVM.m
?????文件????????5002??2010-11-02?15:06??boosting_demo\LinearRegression.m
?????文件?????????784??2010-11-02?15:03??boosting_demo\CART.m
?????文件????????3095??2010-11-02?15:05??boosting_demo\DataGen.m
?????文件????????3166??2010-11-02?15:02??boosting_demo\Adaboost.m
?????文件????????1395??2010-11-02?15:10??boosting_demo\Stump.m
?????文件????????1319??2014-02-12?13:18??license.txt