Finite algorithms for Huber's m estimator
SIAM Journal on Scientific and Statistical Computing
Numerical methods foB]robust regression: linear models
SIAM Journal on Scientific and Statistical Computing
Lipschitz continuity of solutions of linear inequalities, programs and complementarity problems
SIAM Journal on Control and Optimization
The nature of statistical learning theory
The nature of statistical learning theory
Improved generalization via tolerant training
Journal of Optimization Theory and Applications
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
The Linear l1 Estimator and the Huber M-Estimator
SIAM Journal on Optimization
Voting method for the detection of subpixel flow field
Pattern Recognition Letters
Object-oriented software for quadratic programming
ACM Transactions on Mathematical Software (TOMS)
The Journal of Machine Learning Research
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Kernel Underestimation of Functions with Multiple Local Minima
Computational Optimization and Applications
Geometric image registration under locally variant illuminations using Huber M-estimator
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
An optimized tongue image color correction scheme
IEEE Transactions on Information Technology in Biomedicine
A regularized correntropy framework for robust pattern recognition
Neural Computation
A multiresolution wavelet kernel for support vector regression
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Support vector regression for surveillance purposes
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
A sparse kernel algorithm for online time series data prediction
Expert Systems with Applications: An International Journal
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The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and nonlinear support vector estimators. Previous models were significantly more complex or formulated in the dual space and most involved specialized numerical algorithms for solving the robust Huber linear estimator [3], [6], [12], [13], [14], [23], [28]. Numerical test comparisons with these algorithms indicate the computational effectiveness of the new quadratic programming model for both linear and nonlinear support vector problems. Results are shown on problems with as many as 20,000 data points, with considerably faster running times on larger problems.