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07283cam a2200397 i 4500 |
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19184960 |
| 003 - CONTROL NUMBER IDENTIFIER |
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CITU |
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20230925123943.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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160721s2017 mau b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2016948470 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780128042915 |
| 040 ## - CATALOGING SOURCE |
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DLC |
| Language of cataloging |
eng |
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rda |
| Transcribing agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9.D343 |
| Item number |
W58 2017 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Witten, I. H. (Ian H.) |
| Relator term |
author. |
| 245 00 - TITLE STATEMENT |
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| Title |
Data mining : |
| Remainder of title |
practical machine learning tools and techniques / |
| Statement of responsibility, etc. |
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
Fourth Edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Cambridge, MA; |
| -- |
Amsterdam : |
| Name of producer, publisher, distributor, manufacturer |
Morgan Kaufmann, |
| Date of production, publication, distribution, manufacture, or copyright notice |
[2017] |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
c2017 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxxii, 621 pages ; |
| Dimensions |
24 cm |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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unmediated |
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n |
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rdamedia |
| 338 ## - CARRIER TYPE |
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volume |
| Carrier type code |
nc |
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rdacarrier |
| 500 ## - GENERAL NOTE |
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| General note |
Rev. edition of: Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references (pages 573-601) and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Table of Contents<br/><br/>Part I: Introduction to data mining<br/><br/>Chapter 1. What?s it all about?<br/><br/> Abstract<br/> 1.1 Data Mining and Machine Learning<br/> 1.2 Simple Examples: The Weather Problem and Others<br/> 1.3 Fielded Applications<br/> 1.4 The Data Mining Process<br/> 1.5 Machine Learning and Statistics<br/> 1.6 Generalization as Search<br/> 1.7 Data Mining and Ethics<br/> 1.8 Further Reading and Bibliographic Notes<br/><br/>Chapter 2. Input: Concepts, instances, attributes<br/><br/> Abstract<br/> 2.1 What?s a Concept?<br/> 2.2 What?s in an Example?<br/> 2.3 What?s in an Attribute?<br/> 2.4 Preparing the Input<br/> 2.5 Further Reading and Bibliographic Notes<br/><br/>Chapter 3. Output: Knowledge representation<br/><br/> Abstract<br/> 3.1 Tables<br/> 3.2 Linear Models<br/> 3.3 Trees<br/> 3.4 Rules<br/> 3.5 Instance-Based Representation<br/> 3.6 Clusters<br/> 3.7 Further Reading and Bibliographic Notes<br/><br/>Chapter 4. Algorithms: The basic methods<br/><br/> Abstracts<br/> 4.1 Inferring Rudimentary Rules<br/> 4.2 Simple Probabilistic Modeling<br/> 4.3 Divide-and-Conquer: Constructing Decision Trees<br/> 4.4 Covering Algorithms: Constructing Rules<br/> 4.5 Mining Association Rules<br/> 4.6 Linear Models<br/> 4.7 Instance-Based Learning<br/> 4.8 Clustering<br/> 4.9 Multi-instance Learning<br/> 4.10 Further Reading and Bibliographic Notes<br/> 4.11 Weka Implementations<br/><br/>Chapter 5. Credibility: Evaluating what?s been learned<br/><br/> Abstract<br/> 5.1 Training and Testing<br/> 5.2 Predicting Performance<br/> 5.3 Cross-Validation<br/> 5.4 Other Estimates<br/> 5.5 Hyperparameter Selection<br/> 5.6 Comparing Data Mining Schemes<br/> 5.7 Predicting Probabilities<br/> 5.8 Counting the Cost<br/> 5.9 Evaluating Numeric Prediction<br/> 5.10 The MDL Principle<br/> 5.11 Applying the MDL Principle to Clustering<br/> 5.12 Using a Validation Set for Model Selection<br/> 5.13 Further Reading and Bibliographic Notes<br/><br/>Part II: More advanced machine learning schemes<br/><br/>Chapter 6. Trees and rules<br/><br/> Abstract<br/> 6.1 Decision Trees<br/> 6.2 Classification Rules<br/> 6.3 Association Rules<br/> 6.4 Weka Implementations<br/><br/>Chapter 7. Extending instance-based and linear models<br/><br/> Abstract<br/> 7.1 Instance-Based Learning<br/> 7.2 Extending Linear Models<br/> 7.3 Numeric Prediction With Local Linear Models<br/> 7.4 Weka Implementations<br/><br/>Chapter 8. Data transformations<br/><br/> Abstracts<br/> 8.1 Attribute Selection<br/> 8.2 Discretizing Numeric Attributes<br/> 8.3 Projections<br/> 8.4 Sampling<br/> 8.5 Cleansing<br/> 8.6 Transforming Multiple Classes to Binary Ones<br/> 8.7 Calibrating Class Probabilities<br/> 8.8 Further Reading and Bibliographic Notes<br/> 8.9 Weka Implementations<br/><br/>Chapter 9. Probabilistic methods<br/><br/> Abstract<br/> 9.1 Foundations<br/> 9.2 Bayesian Networks<br/> 9.3 Clustering and Probability Density Estimation<br/> 9.4 Hidden Variable Models<br/> 9.5 Bayesian Estimation and Prediction<br/> 9.6 Graphical Models and Factor Graphs<br/> 9.7 Conditional Probability Models<br/> 9.8 Sequential and Temporal Models<br/> 9.9 Further Reading and Bibliographic Notes<br/> 9.10 Weka Implementations<br/><br/>Chapter 10. Deep learning<br/><br/> Abstract<br/> 10.1 Deep Feedforward Networks<br/> 10.2 Training and Evaluating Deep Networks<br/> 10.3 Convolutional Neural Networks<br/> 10.4 Autoencoders<br/> 10.5 Stochastic Deep Networks<br/> 10.6 Recurrent Neural Networks<br/> 10.7 Further Reading and Bibliographic Notes<br/> 10.8 Deep Learning Software and Network Implementations<br/> 10.9 WEKA Implementations<br/><br/>Chapter 11. Beyond supervised and unsupervised learning<br/><br/> Abstract<br/> 11.1 Semisupervised Learning<br/> 11.2 Multi-instance Learning<br/> 11.3 Further Reading and Bibliographic Notes<br/> 11.4 WEKA Implementations<br/><br/>Chapter 12. Ensemble learning<br/><br/> Abstract<br/> 12.1 Combining Multiple Models<br/> 12.2 Bagging<br/> 12.3 Randomization<br/> 12.4 Boosting<br/> 12.5 Additive Regression<br/> 12.6 Interpretable Ensembles<br/> 12.7 Stacking<br/> 12.8 Further Reading and Bibliographic Notes<br/> 12.9 WEKA Implementations<br/><br/>Chapter 13. Moving on: applications and beyond<br/><br/> Abstract<br/> 13.1 Applying Machine Learning<br/> 13.2 Learning From Massive Datasets<br/> 13.3 Data Stream Learning<br/> 13.4 Incorporating Domain Knowledge<br/> 13.5 Text Mining<br/> 13.6 Web Mining<br/> 13.7 Images and Speech<br/> 13.8 Adversarial Situations<br/> 13.9 Ubiquitous Data Mining<br/> 13.10 Further Reading and Bibliographic Notes<br/> 13.11 WEKA Implementations<br/><br/>Appendix A. Theoretical foundations<br/><br/> A.1 Matrix Algebra<br/> A.2 Fundamental Elements of Probabilistic Methods<br/><br/>Appendix B. The WEKA workbench<br/><br/> B.1 What?s in WEKA?<br/> B.2 The package management system<br/> B.3 The Explorer<br/> B.4 The Knowledge Flow Interface<br/> B.5 The Experimenter |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Description<br/><br/>Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.<br/><br/>Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. View more ><br/>Key Features<br/><br/> Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects<br/> Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods<br/> Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface<br/> Includes open-access online courses that introduce practical applications of the material in the book |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Data mining. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Frank, Eibe |
| Relator term |
author |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hall, Mark A. |
| Relator term |
author |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Pal, Christopher J. |
| Dates associated with a name |
author |
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7 |
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cbc |
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orignew |
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2 |
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epcn |
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20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
BOOK |
| Koha issues (borrowed), all copies |
2 |