The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
More generality in efficient multiple kernel learning
Abstract
Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient descent based optimization schemes has made it possible to tackle large scale problems. Simultaneously, MKL based algorithms have achieved very good results on challenging real world applications. Yet, despite their successes, MKL approaches are limited in that they focus on learning a linear combination of given base kernels.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002) · Machine Learning · doi:10.1023/a:1012450327387 · Cited by 2,206
Multiple kernel learning, conic duality, and the SMO algorithm
Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453
Large Scale Multiple Kernel Learning
Sonnenburg, S., Rätsch, G., Schäfer, C., & Schölkopf, B. (2006) · MPG.PuRe (Max Planck Society) · doi:10.5555/1248547.1248604 · Cited by 1,201
Cristianini, N., Kandola, J., Elisseeff, A., & Shawe-Taylor, J. (2006) · Studies in Fuzziness and Soft Computing · doi:10.1007/3-540-33486-6_8 · Cited by 837
Learning gender with support faces
Moghaddam, B., & Yang, M.-H. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000244 · Cited by 598
Learning The Discriminative Power-Invariance Trade-Off
Varma, M., & Ray, D. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408875 · Cited by 520
Self-citationScalable training of L1-regularized log-linear models
Andrew, G., & Gao, J. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273501 · Cited by 506
Supervised feature selection via dependence estimation
Song, L., Smola, A., Gretton, A., Borgwardt, K. M., & Bedo, J. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273600 · Cited by 379
Boosting Sex Identification Performance
Baluja, S., & Rowley, H. A. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-8910-9 · Cited by 350
Learning the Kernel with Hyperkernels
Ong, C. S., Smola, A. J., Williamson, R. C., & Herbrich, R. (2005) · ANU Open Research (Australian National University) · Cited by 324
Cited by
Methodologies for Cross-Domain Data Fusion: An Overview
Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454
Multiple Kernel Fuzzy Clustering
Huang, H.-C., Chuang, Y.-Y., & Chen, C.-S. (2012) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2011.2170175 · Cited by 406
Multiple kernel learning for emotion recognition in the wild
Sikka, K., Dykstra, K., Sathyanarayana, S., Littlewort, G., & Bartlett, M. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531741 · Cited by 119
Facial expression recognition in the wild based on multimodal texture features
Sun, B., Li, L., Zhou, G., & He, J. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061407 · Cited by 74
Zhao, J., Chen, L., Pedrycz, W., & Wang, W. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2815997 · Cited by 50
Metadata
- Authors
- Varma, M., & Babu, B. R.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 1065-1072
- OpenAlex
- W2127069950
- Cited by
- 398
- References
- 18
- FWCI
- 19.98
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: More generality in efficient multiple kernel learning. https://app.cochat.ai/papers/10.1145/1553374.1553510
- The paper
- Varma, M., & Babu, B. R. (2009). More generality in efficient multiple kernel learning. Proceedings of the 26th Annual International Conference on Machine Learning. https://doi.org/10.1145/1553374.1553510
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.
