Data mining : practical machine learning tools and techniques.: practical machine learning tools and techniques. (2016)

• Record Type: Book
• Authors: Witten, I. H (Ian H.); Frank, Eibe; Hall, Mark A (Mark Andrew); Pal, Christopher J
• Contents: PART I: INTRODUCTION TO DATA MINING 1. WHAT’S IT ALL ABOUT? 2. INPUT: CONCEPTS, INSTANCES, ATTRIBUTES 3. OUTPUT: KNOWLEDGE REPRESENTATION 4. ALGORITHMS: THE BASIC METHODS 5. CREDIBILITY: EVALUATING WHAT’S BEEN LEARNED PART II: IMPLEMENTATIONS OF MODERN MACHINE LEARNING SCHEMES 6. TREES AND RULES 7. EXTENDING INSTANCE BASED METHODS AND LINEAR MODELS 8. DATA TRANSFORMATIONS 9. PROBABILISTIC METHODS 10. DEEP LEARNING 11. BEYOND SUPERVISED VS UNSUPERVISED LEARNING 12. ENSEMBLE METHODS 13. MOVING ON: APPLICATIONS AND BEYOND PART III: THE WEKA DATA MINING WORKBENCH 14. INTRODUCTION TO WEKA 15. THE EXPLORER 16. THE KNOWLEDGE FLOW INTERFACE 17. THE EXPERIMENTER 18. THE COMMAND-LINE INTERFACE 19. EMBEDDED MACHINE LEARNING 20. WRITING NEW LEARNING SCHEMES 21. TUTORIAL EXERCISES FOR THE WEKA EXPLORER REFERENCES APPENDIX 1. Matrix Algebra Review APPENDIX 2. Probability Distributions
• Edition: Fourth edition
• Publisher Details: Amsterdam : Morgan Kaufmann
• Publication Date: 2016
• Extent: 1 online resource, illustrations
• Subjects: 006.312; Data mining
• Languages: English
• ISBNs: 9780128043578
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.