621.txt

This complete matlab for neural network

发信人: yaomc (白头翁&山东大汉), 信区: DataMining
标  题: [合集]有谁用过cart的？
发信站: 南京大学小百合站 (Fri Nov 30 10:44:09 2001), 站内信件

study (.net) 于Mon Oct 15 13:19:58 2001提到：

谁知道cart怎么用啊？
大概介绍一下使用的感受和模块功能
如何？


roamingo (漫步鸥) 于Mon Oct 15 19:31:42 2001提到：

对, 介绍一下吧! 最好还有输入输出的数据格式.



yaomc (白头翁&山东大汉) 于Mon Oct 15 19:54:06 2001提到：

不知道看看相关的帮助是否可以解决问题？



study (.net) 于Tue Oct 16 00:45:58 2001提到：

这东西好像很难搞定，如果没有一定的理论知识！
希望有高手先介绍一下！



yaomc (白头翁&山东大汉) 于Tue Oct 16 08:24:43 2001提到：

确实没有用过。不过，好像是决策分类的东西，是不是啊？


matrix (天行健，君子以自强不息) 于Tue Oct 16 09:44:17 2001)
提到：

有人做讲座吗？


yaomc (白头翁&山东大汉) 于Tue Oct 16 09:46:03 2001提到：

现在好像还没有。
也说不定过一段时间那位专家就给大家上一课呢。
呵呵。


roamingo (漫步鸥) 于Tue Oct 16 09:58:21 2001提到：

A quick Google search:

CART: A Brief Description

CART stands for "Classification and Regression Trees", and, as it's name 
implies, it is a method of categorizing very large sets of data, very 
quickly. You often hear the term "Data-mining" used these days to describe 
the process of analyzing data to find relationships between elements, or 
building a predictive system to explain the data. Most of the data-mining 
done today involves classification, and therefore can benefit greatly 
from the use of CART. 

CART was introduced in 1984 by Leo Breiman, Jerome Friedman, Richard Olshen,
and Charles Stone; all UC Berkeley and Stanford statisticians. Their CART 
methodology has vastly improved various performance bottlenecks, as well as 
problems in accuracy. Some of this was achieved by using only binary 
splitting, and by introducing automatic tree testing. They also came up 
with a completely different, and improved method for dealing with missing 
values.

More information (example, bibliography): 

    http://www.cs.mcgill.ca/~mbatch/CART.html 

CART FAQ: http://www.timberlake.co.uk/software/cart/cartfaq.htm

CART 4.0 是决策树(decision tree)和回归树(regression tree)的商业化软件, 有很强
的理论背景.