소장자료
| LDR | 01995cam a22003018a 4500 | ||
| 001 | 0091882218▲ | ||
| 005 | 20180520134014▲ | ||
| 008 | 101005s2011 maua 000 0 eng ▲ | ||
| 010 | ▼a2010039827▲ | ||
| 020 | ▼a9780123748560 (pbk.)▲ | ||
| 035 | ▼a(KERIS)REF000016085281▲ | ||
| 040 | ▼aDLC▼cDLC▼d221016▲ | ||
| 042 | ▼apcc▲ | ||
| 050 | 0 | 0 | ▼aQA76.9.D343▼bW58 2011▲ |
| 082 | 0 | 0 | ▼a006.3/12▼221▲ |
| 090 | ▼a006.3▼bW829d3▲ | ||
| 100 | 1 | ▼aWitten, I. H.▼q(Ian H.)▲ | |
| 245 | 1 | 0 | ▼aData mining :▼bpractical machine learning tools and techniques.▲ |
| 250 | ▼a3rd ed. /▼bIan H. Witten, Frank Eibe, Mark A. Hall.▲ | ||
| 260 | ▼aBurlington, MA :▼bMorgan Kaufmann,▼cc2011.▲ | ||
| 263 | ▼a1102▲ | ||
| 300 | ▼axxxiii, 629 p. :▼bill. ;▼c24 cm.▲ | ||
| 490 | 0 | ▼aThe Morgan Kaufmann series in data management systems▲ | |
| 505 | 8 | ▼aMachine generated contents note: PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: 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 Mining.Ch 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.▲ | |
| 520 | ▼aOffers 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 work on data mining and machine learning teaches you what you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods. ▲ | ||
| 650 | 0 | ▼aData mining.▲ | |
| 700 | 1 | ▼aHall, Mark A.▲ | |
| 999 | ▼c정영주▲ |
Data mining :practical machine learning tools and techniques
자료유형
국외단행본
서명/책임사항
Data mining : practical machine learning tools and techniques.
개인저자
판사항
3rd ed.
발행사항
Burlington, MA : Morgan Kaufmann , c2011.
형태사항
xxxiii, 629 p. : ill. ; 24 cm.
내용주기
Machine generated contents note: PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: 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 Mining.Ch 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.
요약주기
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 work on data mining and machine learning teaches you what you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods.
ISBN
9780123748560 (pbk.)
청구기호
006.3 W829d3
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