25.txt

This complete matlab for neural network

发信人: chdq135 (阿权), 信区: DataMining
标  题: 一个机器学习的好网站（求电子图书）
发信站: 南京大学小百合站 (Wed Oct 16 21:33:09 2002), 站内信件

机器学习的网站。有java写的源代码
Weka Machine Learning Project
An exciting and potentially far-reaching development in contemporary compute
r science is the invention and application of methods of machine learning. T
hese enable a computer program to automatically analyse a large body of data
 and decide what information is most relevant. This crystallised information
 can then be used to help people make decisions faster and more accurately.
One of the central problems of the information age is dealing with the enorm
ous explosion in the amount of raw information that is available. Machine le
arning (ML) has the potential to sift through this mass of information and c
onvert it into knowledge that people can use. So far, however, it has been u
sed mainly on small problems under well controlled conditions. Our aim is to
 bring the technology out of the laboratory and provide solutions that can m
ake a difference to people.
The overall goal of this research programme is to build a state-of-the-art f
acility for developing techniques of ML and to investigate their application
 to agriculture, a key area of the New Zealand economy.
Our team has incorporated several standard ML techniques into a software "wo
rkbench" called WEKA, for Waikato Environment for Knowledge Analysis. With i
t, a specialist in a particular field is able to use ML to derive useful kno
wledge from databases that are far too large to be analysed by hand. WEKA's 
users are ML researchers and industrial scientists. WEKA has already been us
ed to help determine what information dairy farmers use in deciding which co
ws to keep in their herds. It has also been applied to mushroom classificati
on and the analysis of milk production.
Our objectives are to
make ML techniques generally available;
apply them to practical problems in agriculture;
develop new machine learning algorithms and give them to the world;
contribute to a theoretical framework for the field.
Our machine learning package is publically available and presents a collecti
on of algorithms for solving real-world data mining problems. The software i
s written entirely in Java and includes a uniform interface to a number of s
tandard ML techniques. Please feel free to browse around.

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