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140122 ||| eng |
| 020 |
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|a 9783540286509
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| 100 |
1 |
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|a Bousquet, Olivier
|e [editor]
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| 245 |
0 |
0 |
|a Advanced Lectures on Machine Learning
|h Elektronische Ressource
|b ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures
|c edited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch
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| 250 |
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|a 1st ed. 2004
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2004, 2004
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| 300 |
|
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|a X, 246 p
|b online resource
|
| 505 |
0 |
|
|a An Introduction to Pattern Classification -- Some Notes on Applied Mathematics for Machine Learning -- Bayesian Inference: An Introduction to Principles and Practice in Machine Learning -- Gaussian Processes in Machine Learning -- Unsupervised Learning -- Monte Carlo Methods for Absolute Beginners -- Stochastic Learning -- to Statistical Learning Theory -- Concentration Inequalities
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| 653 |
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|a Computer science
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| 653 |
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|a Computer Science
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Algorithms
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Theory of Computation
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Pattern recognition systems
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| 700 |
1 |
|
|a Luxburg, Ulrike von
|e [editor]
|
| 700 |
1 |
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|a Rätsch, Gunnar
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b SBA
|a Springer Book Archives -2004
|
| 490 |
0 |
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|a Lecture Notes in Artificial Intelligence
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| 028 |
5 |
0 |
|a 10.1007/b100712
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/b100712?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 006.3
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| 520 |
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|a Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning
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