Call number:
M 09.0096
Description / Table of Contents:
Contents: Preface 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 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
Type of Medium:
Monograph available for loan
Pages:
xxxi, 525 p.
,
ill
,
24 cm
Edition:
2nd ed.
ISBN:
0120884070
Series Statement:
Morgan Kaufmann series in data management systems
Classification:
Informatics
Location:
Upper compact magazine
Branch Library:
GFZ Library
