The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
On Kernel Target Alignment
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Citations
Cites
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Self-citationLearning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,789
An Introduction to Support Vector Machines
Cristianini, N., & Shawe-Taylor, J. S. (2000) · ePrints Soton (University of Southampton) · Cited by 4,115
Self-citationA Probabilistic Theory of Pattern Recognition
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Sparse Greedy Matrix Approximation for Machine Learning
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Learning Kernel Classifiers: Theory and Algorithms
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Structural risk minimization over data-dependent hierarchies
Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C., & Anthony, M. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.705570 · Cited by 527
Self-citationTheory of Reproducing Kernels and Its Applications
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Cristianini, N., Shawe-Taylor, J., Elisseeff, A., & Kandola, J. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0052 · Cited by 474
Self-citationLearning the Kernel Matrix with Semi-Definite Programming
Lanckriet, G. R. G., Christianini, N., Bartlett, P. L., Ghaoui, L. E., & Jordan, M. I. (2002) · QUT ePrints (Queensland University of Technology) · doi:10.5555/1005332.1005334 · Cited by 386
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Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces
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Cited by
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
“For example Cristianini et al. [2002] define the alignment between a Gram matrix K and the corresponding +1/− 1 vector of alignment targets y as A(K,y) = y>Ky n‖K‖F , (5.”
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Riemannian Approaches in Brain-Computer Interfaces: A Review
Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420
Learning the Kernel Matrix with Semi-Definite Programming
Lanckriet, G. R. G., Christianini, N., Bartlett, P. L., Ghaoui, L. E., & Jordan, M. I. (2002) · QUT ePrints (Queensland University of Technology) · doi:10.5555/1005332.1005334 · Cited by 386
KBA: kernel boundary alignment considering imbalanced data distribution
Wu, G., & Chang, E. Y. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.95 · Cited by 380
Metadata
- Authors
- Cristianini, N., Kandola, J., Elisseeff, A., & Shawe-Taylor, J.
- Venue
- Studies in Fuzziness and Soft Computing
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 205-256
- ISSN
- 1434-9922 (ISSN-L)
- OpenAlex
- W2142387771
- Cited by
- 837
- References
- 19
- FWCI
- 96.70
- Type
- book chapter
- Language
- en
- Open access
- closed
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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.
