The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predicting time series with support vector machines
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Self-citation
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Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Self-citationForecasting of photovoltaic power generation and model optimization: a review
Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.017 · Cited by 1,211
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Self-citationTowards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A., & Mikolov, T. (2015) · arXiv · doi:10.48550/arxiv.1502.05698 · Cited by 670
Improving Multi-Step Prediction of Learned Time Series Models
Venkatraman, A., Hebert, M., & Bagnell, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9590 · Cited by 198
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K. K., Prabowo, A., Rahaman, M. S., & Salim, F. D. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474838 · Cited by 127
Support vector regression for link load prediction
Bermolen, P., & Rossi, D. (2009) · Computer Networks · doi:10.1016/j.comnet.2008.09.018 · Cited by 121
Application of support vector regression for modeling low flow time series
Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-018-0128-1 · Cited by 56
Emotional Expression Classification Using Time-Series Kernels
Lorincz, A., Jeni, L. A., Szabo, Z., Cohn, J. F., & Kanade, T. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2013.131 · Cited by 45
Balcıoğlu, H. E., & Seçkin, A. Ç. (2021) · Archive of Applied Mechanics · doi:10.1007/s00419-020-01765-5 · Cited by 40
Finizola, J. S., Targino, J. M., Teodoro, F. G. S., & de Moraes Lima, C. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-03928-8_18 · Cited by 9
Metadata
- Authors
- Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1997
- Pages
- 999-1004
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1663792126
- Cited by
- 981
- References
- 1
- FWCI
- 5.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
