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Data mining : practical machine learning tools and techniques 4th ed

Data mining : practical machine learning tools and techniques 4th ed (Loan 12 times)

Material type
단행본
Personal Author
Witten, I. H. (Ian H.). Witten, I. H. (Ian H.).
Title Statement
Data mining : practical machine learning tools and techniques / Ian H. Witten ... [et al.].
판사항
4th ed.
Publication, Distribution, etc
Amsterdam :   Elsevier,   c2017.  
Physical Medium
xxxii, 621 p. ; 24 cm.
ISBN
9780128042915
General Note
Rev. edition of: Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013.  
Bibliography, Etc. Note
Includes bibliographical references (p. 573-601) and index.
Subject Added Entry-Topical Term
Data mining.
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001 000045902513
005 20170410113457
008 170407s2017 ne b 001 0 eng d
010 ▼a 2016948470
020 ▼a 9780128042915
035 ▼a (KERIS)REF000018319208
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
050 0 0 ▼a QA76.9.D343 ▼b W58 2017
082 0 0 ▼a 006.3/12 ▼2 23
084 ▼a 006.312 ▼2 DDCK
090 ▼a 006.312 ▼b W829d4
245 0 0 ▼a Data mining : ▼b practical machine learning tools and techniques / ▼c Ian H. Witten ... [et al.].
250 ▼a 4th ed.
260 ▼a Amsterdam : ▼b Elsevier, ▼c c2017.
300 ▼a xxxii, 621 p. ; ▼c 24 cm.
500 ▼a Rev. edition of: Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013.
504 ▼a Includes bibliographical references (p. 573-601) and index.
650 0 ▼a Data mining.
700 1 ▼a Witten, I. H. ▼q (Ian H.).
700 1 ▼a Witten, I. H. ▼q (Ian H.). ▼t Data mining.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info/ Call Number 006.312 W829d4 Accession No. 121239838 Availability Course Reserve Due Date Make a Reservation Service M
No. 2 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.312 W829d4 Accession No. 121248298 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Part I : Introduction to data mining. What''s it all about?
Input : concepts, instances, and attributes
Output : knowledge representation
Algorithms : the basic methods
Credibility : evaluating what''s been learned
Part II : More advanced machine learning schemes. Trees and rules
Extending instance-based and linear models
Data transformations
Probabilistic methods
Deep learning
Beyond supervised and unsupervised learning
Ensemble learning
Moving on : applications and beyond.

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