ACStsp

MATLAB的神经网络工具箱实用指南. 第一章是神经网络的基本介绍，第二章包括了由工具箱指定的有关网络结构和符号的基本材料以及建立神经网络的一些基本函数，例如new、init、adapt和train。第三章以反向传播网络为例讲解了反向传播网络的原理和应用的基本过程。(MATLAB neural network toolbox of practical guidance. The first chapter is a basic introduction to neural networks, and the second chapter, including the toolbox designated by the network structure and symbols related to the basic material and the establishment of neural network, some basic functions, such as new, init, adapt and train. Chapter III to back-propagation network as an example to explain the back-propagation network, the basic principle and application process.)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)

曼彻斯特编码，纯属原创。所有参数开放，易于使用。大家发表意见。(Manchester encoding is purely original. All the parameters open, easy to use. Everyone to express their views.)

关于统计学习理论的外国经典教材，但需要一定的数学功底，对进行机器学习研究等方面很有帮助(stactics)

说明：  禁忌算法MATLAB代码，优化算法，处理各种寻优问题(Tabu Search MATLABcode)

包含较为多的matlab和stk混合编程资料(Contains the matlab and stk mixed programming information)

电力系统标准测试系统，30节点系统，格式完全正确，可以方便的用来分析计算。(Power system standard test system, 30-bus system, the format entirely correctly, can easily be used to analyze the calculation.)

A small Blind Source Separation example

经验模态分解(Empirical Mode Decomposition, 简称EMD))方法的MATLAB代码(EMD (Empirical Mode Decomposition, referred to as EMD)) ways of MATLAB code)

Adaboost算法是一类弱分类器，本算法使用adaboost算法实现对于训练数据划分成两个组，然后可以用来进行数据的分类。(Adaboost algorithm is a class of weak classifier, the algorithm uses adaboost algorithm for the training data into two groups, and then can be used for data classification.)