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SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regula
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SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike reg
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a document about Classification and Regression Trees
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《Software for Flexible Bayesian Modeling and Markov Chain Sampling》是机器学习领域专家Neal编写的用于Bayesian和马尔可夫链Linux下的C语言工具包。很有名，也很权威。
-This software supports Bayesian regression and classification models based on neural networks and Gaussian processes, and Ba
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Classification and Regression Tree的ppt介绍，简称CART，即分类回归树。-Ppt Classification and Regression Tree of the introduction, referred to as CART, the classification and regression trees.
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MATLAB cross-validation tool for classification and regression v0.1
FEATURES:
+ K-fold cross validation.
+ Arbitrary train and prediction functions with parameters can be used.
+ Arbitrary loss function can be used.
+ Wrappers for
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使用matlabR2007a编写的分类回归树代码，已经调试，可以运行-Prepared using matlabR2007a code classification and regression trees have been debugging, you can run
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数据挖掘教程：分类与回归树模型的教程介绍-Classification and regression trees documentation
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该计算统计学工具箱包含倒靴法，分类和回归树，空间点过程，MCMC以及其他很多算法。-This toolbox contains functions for bootstrap, jackknife, classification and regression trees, projection pursuit, spatial point processes, MCMC, and many others.
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Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a t
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包括无监督和监督的机器学习技术
• K-means and other clustering tools
• Neural Networks
• Decision trees and ensemble learning
• Naï ve Bayes Classification
• Linear, logistic and nonlinear regression-Highlights include unsu
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随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法
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随机森林算法的构造过程：1、通过给定的原始数据，选出其中部分数据进行决策树的构造，数据选取是”有放回“的过程，我在这里用的是CART分类回归树。
2、随机森林构造完成之后，给定一组测试数据，使得每个分类器对其结果分类进行评估，最后取评估结果的众数最为最终结果-Random Forest algorithm construction process: 1, by a given raw data, which part of the decision tree data structu
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