The DOI matches its OpenAlex record; 2 of the 2 references it cites were found in a registry or an index.
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
Abstract
We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived for designing regression functions which are robust to uncertainties in the regression setting. The proposed formulations are independent of the underlying distribution, requiring only the existence of second order moments. These formulations are then specialized to the case of missing values in observations for both classification and regression problems. Experiments show that the proposed formulations outperform imputation.
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Training with Noise is Equivalent to Tikhonov Regularization
Bishop, C. M. (1995) · Neural Computation · doi:10.1162/neco.1995.7.1.108 · Cited by 1,325
Cited by
Kernel Mean Embedding of Distributions: A Review and Beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., & Schölkopf, B. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000060 · Cited by 409
Xu, H., & Mannor, S. (2012) · Machine Learning · doi:10.1007/s10994-011-5268-1 · Cited by 377
(2013) · Cited by 131
Metadata
- Authors
- Shivaswamy, P. K., Bhattacharyya, C., & Smola, A. J.
- Venue
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore)
- Publisher
- Indian Institute of Science Bangalore
- Published
- 2006-12-01
- Volume
- 7
- Issue
- 47
- Pages
- 1283-1314
- DOI
- 10.5555/1248547.1248594
- OpenAlex
- W2147609908
- Cited by
- 197
- References
- 2
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shivaswamy, P. K., Bhattacharyya, C., & Smola, A. J. (2006). Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. NOT FOUND REPOSITORY (Indian Institute of Science Bangalore). https://doi.org/10.5555/1248547.1248594
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