C4.5

Programs for Machine Learning

By: Ross Quinlan, J. R. Quinlan

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Paperback | 1 October 1992

At a Glance

Paperback
316 Pages

Dimensions(cm)
23.5 x 19.05 x 1.68

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Note: this title is also available as a package with C4.5 software for Sun systems. Software can also be purchased separately if desired.

Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation.

C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies.

This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Science Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems

Introduction
Constructing Decision Trees
Unknown Attribute Values
Pruning Decision Trees
From Trees to Rules
Windowing
Grouping Attribute Values
Interacting with Classification Models
Guide to Using the System
Limitations
Desirable Additions
Appendix: Program Listings
Table of Contents provided by Publisher. All Rights Reserved.

C4.5

Programs for Machine Learning

At a Glance

Paperback

Shipping

How to return your order

Defective items

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