The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Alpha seeding for support vector machines
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Reoptimization With the Primal-Dual Interior Point Method
SIAM Journal on Optimization
Training v-support vector regression: theory and algorithms
Neural Computation
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Accurate on-line support vector regression
Neural Computation
A tutorial on support vector regression
Statistics and Computing
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Leave-One-Out Bounds for Support Vector Regression Model Selection
Neural Computation
Neural Computation
Bi-level path following for cross validated solution of kernel quantile regression
Proceedings of the 25th international conference on Machine learning
A New Weighted Support Vector Machine for Regression and Its Parameters Optimization
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A Class of Novel Kernel Functions
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
The Journal of Machine Learning Research
An effective regularization path for ν-support vector classification
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An improved algorithm for the solution of the regularization path of support vector machine
IEEE Transactions on Neural Networks
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
Signal Processing
Model combination for support vector regression via regularization path
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.