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Advances in Kernel Methods: Support Vector Learning
Abstract
The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area. Contributors Peter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kreßel, Davide Mattera, Klaus-Robert Müller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rätsch, Bernhard Schölkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson
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Cited by
Buciluǎ, C., Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150464 · Cited by 2,068
Machine learning: a review of classification and combining techniques
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
Automated classification of bird and amphibian calls using machine learning: A comparison of methods
Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., & Aide, T. M. (2009) · Ecological Informatics · doi:10.1016/j.ecoinf.2009.06.005 · Cited by 317
Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25407 · Cited by 177
Distinct cortical codes and temporal dynamics for conscious and unconscious percepts
Salti, M., Monto, S., Charles, L., King, J.-R., Parkkonen, L., & Dehaene, S. (2015) · eLife · doi:10.7554/elife.05652 · Cited by 121
Metadata
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 1998-12-01
- OpenAlex
- W4312960235
- Cited by
- 761
- References
- 0
- FWCI
- 1.59
- Type
- book
- Language
- en
- Open access
- closed
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