bp-adaboost

代码说明：

说明：  Adaboost 算法的思想是合并多个“弱”分类器的输出以产生有效分类。其主要步骤为 ：首先给出弱学习算法和样本空间（工， y） ，从样本空间中找出 m 组训练数据，每组训练数据的权重都是 1 /m。然后用弱学习算法迭代运算 T 次，每次运算后都按照分类结果更新训练数据权重分布，对于分类失败的训练个体赋予较大权重，下一次迭代运算时更加关注这些训练个体。弱分类器通过反复迭代得到一个分类函数序列 f, ,fz ， … ， Jr ，每个分类函数赋予一个权重，分类结果越好的函数，其对应权重越大。(The idea of the Adaboost algorithm is to combine the output of multiple "weak" classifiers to produce effective classification. The main steps are as follows: Firstly, weak learning algorithm and sample space (Gong, y) are given, and m groups of training data are found from the sample space. The weight of each group of training data is 1/m. Then, the weak learning algorithm is used to iterate T times. After each operation, the weight distribution of training data is updated according to the classification results.)

文件列表：

bp-adaboost\Bp_Ada_Fore.m, 3851 , 2010-11-28
bp-adaboost\Bp_Ada_Sort.m, 4297 , 2010-11-28
bp-adaboost\data.mat, 11820 , 2009-12-28
bp-adaboost\data1.mat, 46394 , 2009-09-09
bp-adaboost, 0 , 2013-08-21