データマイニング：実用機械学習ツールとテクニック（第３版） Data Mining : Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) （3TH）

データマイニング：実用機械学習ツールとテクニック（第３版）<br>Data Mining : Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) （3TH）

電子版価格
¥9,374

電子版あり

データマイニング：実用機械学習ツールとテクニック（第３版）
Data Mining : Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) （3TH）

Witten, Ian H./ Frank, Eibe/ Hall, Mark A.

Morgan Kaufmann Pub（2011/01発売）

ただいまウェブストアではご注文を受け付けておりません。 ⇒古書を探す

製本 Paperback:紙装版/ペーパーバック版／ページ数 629 p.
言語 ENG
商品コード 9780123748560
DDC分類 006.312

基本説明

Includes new material on data transformations, ensemble learning, massive data sets, and multi-instance learning, as well as a new version of the popular Weka machine learning software developed by the authors.

Full Description

Data MiningEdition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

PART IInput: Concepts, Instances, Attributes Ch 3 Output: Knowledge Representation Ch 4 Algorithms: The Basic Methods Ch 5 Credibility: Evaluating What's Been Learned PART II: Advanced Data MiningCh 6 Implementations: Real Machine Learning Schemes Ch 7 Data Transformation Ch 8 Ensemble Learning Ch 9 Moving On: Applications and Beyond PART III: The Weka Data MiningWorkbench Ch 10 Introduction to Weka Ch 11 The Explorer Ch 12 The Knowledge Flow Interface Ch 13 The Experimenter Ch 14 The Command-Line Interface Ch 15 Embedded Machine Learning Ch 16 Writing New Learning Schemes Ch 17 Tutorial Exercises for the Weka Explorer