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2009, ISBN 0521791928, xiii, 349
Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today... 
Kernel functions | Environmental sciences | Neural networks (Computer science) | Machine learning
Book
Foundations and Trends in Machine Learning, ISSN 1935-8237, 2012, Volume 5, Issue 4, pp. 287 - 364
The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods... 
Learning | Data points | Foundations | Distance learning | Mathematical analysis | Data sets | Texts | Nonlinearity
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 08/2011, Volume 33, Issue 8, pp. 1532 - 1547
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 03/2012, Volume 34, Issue 3, pp. 465 - 479
Journal Article
Neurocomputing, ISSN 0925-2312, 12/2017, Volume 267, pp. 210 - 219
Journal Article
IEEE transactions on signal processing, ISSN 1053-587X, 2004, Volume 52, Issue 8, pp. 2165 - 2176
Journal Article
The Annals of statistics, ISSN 0090-5364, 2008, Volume 36, Issue 3, pp. 1171 - 1220
Journal Article
Journal of Machine Learning Research, ISSN 1533-7928, 07/2006, Volume 7, pp. 1531 - 1565
Journal Article
Neurocomputing, ISSN 0925-2312, 02/2015, Volume 149, pp. 253 - 264
Journal Article
Nature (London), ISSN 1476-4687, 2019, Volume 567, Issue 7747, pp. 209 - 212
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 07/2011, Volume 12, pp. 2211 - 2268
... (different representations or different feature subsets). In trying to organize and highlight the similarities and differences between them, we give a taxonomy of and review several multiple kernel learning algorithms... 
Support vector machines | Multiple kernel learning | Kernel machines | support vector machines | PROTEIN FOLD RECOGNITION | CLASSIFICATION | COMBINATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | DATA FUSION | kernel machines | multiple kernel learning | INTEGRATION | MATRICES | SEQUENCE | FRAMEWORK | SUPPORT VECTOR MACHINE | AUTOMATION & CONTROL SYSTEMS
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 03/2015, Volume 37, Issue 3, pp. 597 - 610
We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the... 
Shape | Boosting | Vectors | Mathematical model | Kernel | Equations | Optimization | boosting | Learning feature descriptors | binary embedding | KERNEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Research | Image processing | Mathematical optimization
Journal Article
Pattern Recognition, ISSN 0031-3203, 07/2018, Volume 79, pp. 318 - 327
Journal Article
Pattern recognition, ISSN 0031-3203, 2018, Volume 78, pp. 144 - 153
•Introducing indefinite kernels in multi-class semi-supervised kernel spectral clustering (MSS-KSC) and kernel spectral clustering (KSC, as a special case of... 
Indefinite kernels | Scalable models | Kernel spectral clustering | Semi-supervised learning | Low embedding dimension | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article