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Other titles in the Springer Briefs in Computer Science series:

Demand-Driven Associative Classification (Springerbriefs in Computer Science Springerbriefs in Compute)

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Demand-Driven Associative Classification (Springerbriefs in Computer Science Springerbriefs in Compute) Cover

 

Synopses & Reviews

Publisher Comments:

The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.

Synopsis:

This volume focuses on a major machine learning task known as classification. Some classification problems are hard to solve, but this book shows that they can be decomposed into much simpler sub-problems.

Table of Contents

Introduction and Preliminaries.-Introduction.-The Classification Problem.-Associative Classification.-Demand-Driven Associative Classification.-Extensions to Associative Classification.-Multi-Label Associative Classification.-Competence-Conscious Associative Classification.-Calibrated Associative Classification.-Self-Training Associative Classification.-Ordinal Regression and Ranking.- Conclusions and FutureWork

Product Details

ISBN:
9780857295248
Author:
Veloso, Adriano
Publisher:
Springer
Author:
Meira Jr., Wagner
Author:
Wagner Meira Jr.
Subject:
Associative Rules
Subject:
Data Mining and Knowledge Discovery
Subject:
Probability and Statistics in Computer Science
Subject:
Probability and Statistics in Computer Science <p>First book only devoted to associative classification, which is an emerging classification strategy</p><p>The work puts associative classification algorithms into the existing machine learning theory</p><p
Subject:
Computers-Reference - General
Copyright:
Edition Description:
2011
Series:
SpringerBriefs in Computer Science
Publication Date:
20110527
Binding:
TRADE PAPER
Language:
English
Pages:
125
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Personal Computers » General
Computers and Internet » Software Engineering » Software Management
Health and Self-Help » Health and Medicine » Medical Specialties
History and Social Science » World History » General
Science and Mathematics » Mathematics » Software

Demand-Driven Associative Classification (Springerbriefs in Computer Science Springerbriefs in Compute) New Trade Paper
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Product details 125 pages Springer - English 9780857295248 Reviews:
"Synopsis" by , This volume focuses on a major machine learning task known as classification. Some classification problems are hard to solve, but this book shows that they can be decomposed into much simpler sub-problems.
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