Para ferreira filho, apesar de ser uma declaração bem conhecida, a declaração dos direitos do homem e do cidadão não é a primeira das declarações de direitos. historicamente, tivemos a da virgínia de 1776, que também

WEKA
Machine Learning Algorithms in Java

Ian H. Witten
Department of Computer Science University of Waikato Hamilton, New Zealand E-mail: [e-mail]

Eibe Frank
Department of Computer Science University of Waikato Hamilton, New Zealand E-mail: [e-mail]

This tutorial is Chapter 8 of the book Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Cross-references are to other sections of that book. © 2000 Morgan Kaufmann Publishers. All rights reserved.

chapter eight

Nuts and bolts: Machine learning algorithms in Java

A

ll the algorithms discussed in this book have been implemented and made freely available on the World Wide Web (www.cs.waikato. ac.nz/ml/weka) for you to experiment with. This will allow you to learn more about how they work and what they do. The implementations are part of a system called Weka, developed at the University of Waikato in New Zealand. “Weka” stands for the Waikato Environment for Knowledge Analysis. (Also, the weka, pronounced to rhyme with Mecca, is a flightless bird with an inquisitive nature found only on the islands of New Zealand.) The system is written in Java, an objectoriented programming language that is widely available for all major computer platforms, and Weka has been tested under Linux, Windows, and Macintosh operating systems. Java allows us to provide a uniform interface to many different learning algorithms, along with methods for pre- and postprocessing and for evaluating the result of learning schemes on any given dataset. The interface is described in this chapter. There are several different levels at which Weka can be used. First of all, it provides implementations of state-of-the-art learning algorithms that you can apply to your dataset from the command line. It also includes a variety of tools for transforming datasets, like the algorithms for discretization

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CHAPTER EIGHT | MACHINE LEARNING ALGORITHMS IN JAVA