Sparse multinomial logistic regression: fast algorithms and generalization bounds

@article{Krishnapuram2005SparseML,
  title={Sparse multinomial logistic regression: fast algorithms and generalization bounds},
  author={Balaji Krishnapuram and Lawrence Carin and M{\'a}rio A. T. Figueiredo and Alexander J. Hartemink},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2005},
  volume={27},
  pages={957-968}
}
Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization… CONTINUE READING
Highly Influential
This paper has highly influenced 80 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,128 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 480 extracted citations

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection

IEEE Transactions on Neural Networks and Learning Systems • 2014
View 20 Excerpts
Highly Influenced

Joint Sparse Representation for Robust Multimodal Biometrics Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2014
View 13 Excerpts
Highly Influenced

A Bag-of-Pages Approach to Unordered Multi-page Document Classification

2010 20th International Conference on Pattern Recognition • 2010
View 4 Excerpts
Highly Influenced

Thèse de doctorat de

View 4 Excerpts
Highly Influenced

1,129 Citations

050100150'07'10'13'16'19
Citations per Year
Semantic Scholar estimates that this publication has 1,129 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 38 references

A comparison of numerical optimizers for logistic regression

Thomas P. Minka
2003
View 11 Excerpts
Highly Influenced

Optimization Transfer Using Surrogate Objective Functions

K. Lange, D. Hunter, I. Yang
J. Computational and Graphical Statistics, vol. 9, pp. 1-59, 2000. • 2000
View 4 Excerpts
Highly Influenced

Statistical learning theory

View 20 Excerpts
Highly Influenced

Adaptive Sparseness for Supervised Learning

IEEE Trans. Pattern Anal. Mach. Intell. • 2003
View 3 Excerpts

Similar Papers

Loading similar papers…