Umfang:
Online-Ressource
ISBN:
9783540364344
,
354036434X
Serie:
Lecture Notes in Computer Science 2600
Inhalt:
A Few Notes on Statistical Learning Theory -- A Short Introduction to Learning with Kernels -- Bayesian Kernel Methods -- An Introduction to Boosting and Leveraging -- An Introduction to Reinforcement Learning Theory: Value Function Methods -- Learning Comprehensible Theories from Structured Data -- Algorithms for Association Rules -- Online Learning of Linear Classifiers.
Inhalt:
Machine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11–22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics such as Boosting, Data Mining, Kernel Methods, Logic, Reinforcement Learning, and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, a recorded video of the presentations during the Summer School can be obtained at http://mlg. anu. edu. au/summer2002 It is our hope that graduate students, lecturers, and researchers alike will ?nd this book useful in learning and teaching Machine Learning, thereby continuing the mission of the Summer School. Canberra, November 2002 Shahar Mendelson Alexander Smola Research School of Information Sciences and Engineering, The Australian National University Thanks and Acknowledgments We gratefully thank all the individuals and organizations responsible for the success of the workshop.
Weitere Ausg.:
ISBN 9783540005292
Weitere Ausg.:
Buchausg. u.d.T. Advanced lectures on machine learning Berlin : Springer, 2003 ISBN 3540005293
Sprache:
Englisch
Fachgebiete:
Informatik
Schlagwort(e):
Maschinelles Lernen
;
Maschinelles Lernen
;
Kongress
;
Konferenzschrift
DOI:
10.1007/3-540-36434-X
URL:
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