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Titlebook: Advanced Lectures on Machine Learning; ML Summer Schools 20 Olivier Bousquet,Ulrike Luxburg,Gunnar Rätsch Textbook 2004 Springer-Verlag Ber

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期刊全称Advanced Lectures on Machine Learning
期刊简称ML Summer Schools 20
影响因子2023Olivier Bousquet,Ulrike Luxburg,Gunnar Rätsch
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advanced Lectures on Machine Learning; ML Summer Schools 20 Olivier Bousquet,Ulrike Luxburg,Gunnar Rätsch Textbook 2004 Springer-Verlag Ber
影响因子.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..
Pindex Textbook 2004
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Gaussian Processes in Machine Learning,ign and manufacture. The bolted cantilever, which is the model for a bolted joint between a column and a base on a planer, was used to calrify the relationship between the interface pressure and the logarithmic damping decrement on the bolted joint in the different connecting conditions, such as var
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Stochastic Learning,rnment present the latest research and breakthroughs on how biotechnology is being used to produce economically competitive fuels and chemicals in a sustainable and environmentally responsible manner. The contributors discuss both fundamental science discoveries and the progress that has been made i
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https://doi.org/10.1007/b100712algorithmic learning; bayesian inference; classification; classifier systmes; inductive inference; learni
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Advanced Lectures on Machine Learning978-3-540-28650-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Big Data Application Architecture,er distinct labeling of the data (supervised learning), division of the data into classes (unsupervised learning), selection of the most significant features of the data (feature selection), or a combination of more than one of these tasks.
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Seyed Mehrshad Parvin Hosseini,Aydin Azizi define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.
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