readme

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

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                              README                                   
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                             WEKA 3.4.1
                          27 January 2004
                                                                       
                 Java Programs for Machine Learning 

           Copyright (C) 1998-2004  University of Waikato

                web: http://www.cs.waikato.ac.nz/~ml            
                                                                       
=====================================================================

NOTE FOR USERS WHO HAVE USED WEKA BEFORE: THE LOCATION OF SOME
CLASSIFIERS HAS CHANGED FROM THE LAST RELEASE

weka.classifiers.trees.j48.J48 -> weka.classifiers.trees.J48
weka.classifiers.trees.m5.M5P -> weka.classifiers.trees.M5P
weka.classifiers.trees.lmt.LMT -> weka.classifiers.trees.LMT
weka.classifiers.trees.adtree.ADTree -> weka.classifiers.trees.ADTree
weka.classifiers.rules.part.PART -> weka.classifiers.rules.PART
weka.classifiers.lazy.kstar.KStar -> weka.classifiers.lazy.KStar
weka.classifiers.functions.supportVector.SMO ->
  weka.classifiers.functions.SMO
weka.classifiers.functions.supportVector.SMOreg ->
  weka.classifiers.functions.SMOreg
weka.classifiers.functions.pace.PaceRegression -> 
  weka.classifiers.functions.PaceRegression

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Contents:
---------

1. Using one of the graphical user interfaces in Weka

2. The Weka data format (ARFF)

3. Using Weka from the command line

   - Classifiers
   - Association rules
   - Filters

4. Database access

5. The Experiment package

6. Tutorial

7. Source code

8. Credits

9. Submission of code and bug reports

10. Copyright


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1. Using one of the graphical user interfaces in Weka:
------------------------------------------------------

This assumes that the Weka archive that you have downloaded has been
extracted into a directory containing this README and that you haven't
used an automatic installer (e.g. the one provided for Windows).

Weka 3.4 requires Java 1.4 or higher. Depending on your platform you
may be able to just double-click on the weka.jar icon to run the
graphical user interfaces for Weka. Otherwise, from a command-line
(assuming you are in the directory containing weka.jar), type

java -jar weka.jar

or if you are using Windows use

javaw -jar weka.jar

This will start a small graphical user interface (GUIChooser) from
which you can select the SimpleCLI interface or the more sophisticated
Explorer, Experimenter, and Knowledge Flow interfaces. SimpleCLI just
acts like a simple command shell. The Explorer is currently the main
interface for data analysis using Weka. The Experimenter can be used
to compare the performance of different learning algorithms across
various datasets. The Knowledge Flow provides a component-based
alternative to the Explorer interface.

Example datasets that can be used with Weka are in the sub-directory
called "data", which should be located in the same directory as this
README file.

The Weka user interfaces provide extensive built-in help facilities
(tool tips, etc.). Documentation for the Explorer can be found in
ExplorerGuide.pdf (also in the same directory as this
README). However, this guide is unfortunately not quite up to date
(it's based on Weka 3.3.4).

You can also start the GUIChooser from within weka.jar:

java -classpath weka.jar:$CLASSPATH weka.gui.GUIChooser
or if you are using Windows use
javaw -classpath weka.jar;$CLASSPATH weka.gui.GUIChooser

NOTE FOR PEOPLE WHO WRITE LEARNING SCHEMES WITH WEKA: In case you want
to use your own learning scheme from within one of the graphical user
interfaces (GUIs): one of the components of the GUIs is a generic
object editor that requires a configuration file called
"GenericObjectEditor.props". There is an example file in
weka/gui. This file will be used unless it is overidden by one in your
home directory or the current directory (in that order).  It simply
specifies for each superclass which subclasses to offer as
choices. For example, which Classifiers are available/wanted to be
used when an object requires a property of type Classifier.

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2. The Weka data format (ARFF):
-------------------------------

Datasets for WEKA should be formatted according to the ARFF
format. (However, there are several converters included in WEKA that
can convert other file formats to ARFF. The Weka Explorer will use
these automatically if it doesn't recognize a given file as an ARFF
file.) Examples of ARFF files can be found in the "data" subdirectory.
What follows is a short description of the file format. A more
complete description is available from the Weka web page.

A dataset has to start with a declaration of its name:

@relation name

followed by a list of all the attributes in the dataset (including 
the class attribute). These declarations have the form

@attribute attribute_name specification

If an attribute is nominal, specification contains a list of the 
possible attribute values in curly brackets:

@attribute nominal_attribute {first_value, second_value, third_value}

If an attribute is numeric, specification is replaced by the keyword 
numeric: (Integer values are treated as real numbers in WEKA.)

@attribute numeric_attribute numeric

In addition to these two types of attributes, there also exists a
string attribute type. This attribute provides the possibility to
store a comment or ID field for each of the instances in a dataset:

@attribute string_attribute string

After the attribute declarations, the actual data is introduced by a 

@data

tag, which is followed by a list of all the instances. The instances 
are listed in comma-separated format, with a question mark 
representing a missing value. 

Comments are lines starting with % and are ignored by Weka.

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4. Database access:
-------------------

In terms of database connectivity, you should be able to use any
database with a Java JDBC driver. When using classes that access a
database (e.g. the Explorer), you will probably want to create a
properties file that specifies which JDBC drivers to use, and where to
find the database. This file should reside in your home directory or
the current directory and be called "DatabaseUtils.props". An example
is provided in weka/experiment, this file is used unless it is
overidden by one in your home directory or the current directory (in
that order).

----------------------------------------------------------------------

3. Using Weka from the command line:
------------------------------------

If you want to use Weka from your standard command-line interface
(e.g. bash under Linux):

a) Set WEKAHOME to be the directory which contains this README.
b) Add $WEKAHOME/weka.jar to your CLASSPATH environment variable.
c) Bookmark $WEKAHOME/doc/packages.html in your web browser.

Alternatively you can try using the SimpleCLI user interface available
from the GUI chooser discussed above.

In the following, the names of files assume use of a unix command-line
with environment variables. For other command-lines (including
SimpleCLI) you should substitute the name of the directory where
weka.jar lives for $WEKAHOME. If your platform uses something other
character than / as the path separator, also make the appropriate
substitutions.

===========
Classifiers
===========

Try: