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Keyword

squared error loss

Description: Squared error loss is a statistical loss function that measures prediction error by squaring the difference between estimated and true values.
OpenAlex ID: keywords/squared-error-loss
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
1997 · Pedro Domingos, Michael Pazzani · Machine Learning · 3,106
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A well-conditioned estimator for large-dimensional covariance matrices
2003 · Olivier Ledoit, Michael Wolf · Journal of Multivariate Analysis · 3,024
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Loss Functions for Image Restoration With Neural Networks
2016 · Hang Zhao, Orazio Gallo, et al. · IEEE Transactions on Computational Imaging · 2,654
Estimation with Quadratic Loss
1992 · W. James, Charles Stein · Springer series in statistics · 2,252
"Rational" Expectations, the Optimal Monetary Instrument, and the Optimal Money Supply Rule
1975 · Thomas J. Sargent, Neil Wallace · Journal of Political Economy · 2,170
Optimal Bandwidth Choice for the Regression Discontinuity Estimator
2011 · G. Imbens, K. Kalyanaraman · The Review of Economic Studies · 2,133
Practical selection of SVM parameters and noise estimation for SVM regression
2003 · Vladimir Cherkassky, Yunqian Ma · Neural Networks · 2,033
Inadmissibility of the Usual Estimator for the Mean of a Multivariate Normal Distribution
1956 · Charles Stein · University of California Press eBooks · 1,866
When Is a Linear Control System Optimal?
1964 · R. E. Kalman · Journal of Basic Engineering · 1,072
A dual coordinate descent method for large-scale linear SVM
2008 · Cho-Jui Hsieh, Kai-Wei Chang, et al. · International Conference on Machine Learning · 882
Exponentiated Gradient versus Gradient Descent for Linear Predictors
1997 · Jyrki Kivinen, Manfred K. Warmuth · Information and Computation · 863
Optimal predictive model selection
2004 · Maria Maddalena Barbieri, James O. Berger · The Annals of Statistics · 848
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Boosting With the L 2 Loss
2003 · Peter Bühlmann, Bin Yu · Journal of the American Statistical Association · 846
Least Squares Model Averaging
2007 · Bruce E. Hansen · Econometrica · 840
Bias plus variance decomposition for zero-one loss functions
1996 · Ron Kohavi, David H. Wolpert · 654
(1) Optimal Predictive Model Selection 1
2002 · Maria Maddalena Barbieri, Università Di Roma “la Sapienza, et al. · 640
On Loss Functions for Deep Neural Networks in Classification
2017 · Katarzyna Janocha, Wojciech Marian Czarnecki · Schedae Informaticae · 594
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Deep Learning without Poor Local Minima
2016 · Kawaguchi, Kenji · arXiv (Cornell University) · 592
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An empirical evaluation of supervised learning in high dimensions
2008 · Rich Caruana, Nikos Karampatziakis, et al. · International Conference on Machine Learning · 533
Are Loss Functions All the Same?
2004 · Lorenzo Rosasco, Ernesto De Vito, et al. · Neural Computation · 501
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
2018 · Du, Simon S., Zhai, Xiyu, et al. · arXiv (Cornell University) · 490
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Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
2018 · Zhen-Hua Feng, Josef Kittler, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 485
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Biased and Unbiased Cross-Validation in Density Estimation
1987 · David W. Scott, George R. Terrell · Journal of the American Statistical Association · 485
Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
2011 · Garvesh Raskutti, Martin J. Wainwright, et al. · IEEE Transactions on Information Theory · 456
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On Finite Population Sampling Theory Under Certain Linear Regression Models
1970 · RICHARD M. ROYALL · Biometrika · 435
Works count: 4,342
Citation count: 83,330
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