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Beskrivande textThere is no magic in machine learning; instead there is an identifiable body of practical techniques that can extract useful information from raw data. Describing these techniques and showing how they work, this edition includes thirty technique sections; comprehensive information on neural networks; a section on Bayesian networks; and more.(Bookdata)
RecensionerThis book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate. If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start. --From the foreword by Jim Gray, Microsoft Research(Whitaker)
ÖvrigtIan H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann. Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.(Whitaker)
InnehållsförteckningPreface 1. Whats it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating whats been learned 6. Implementations: Real machine learning schemes 7. Transformations: Engineering the input and output 8. Moving on: Extensions and applications Part II: The Weka machine learning workbench 9. Introduction to Weka 10. The Explorer 11. The Knowledge Flow interface 12. The Experimenter 13. The command-line interface 14. Embedded machine learning 15. Writing new learning schemes References Index(Whitaker)
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