System identification
Knowledge-based artificial neural networks
Artificial Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Automatica (Journal of IFAC)
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Support vector regression with ANOVA decomposition kernels
Advances in kernel methods
Support vector density estimation
Advances in kernel methods
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
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 to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Large Scale Kernel Regression via Linear Programming
Machine Learning
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries
A tutorial on support vector regression
Statistics and Computing
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems
IEEE Transactions on Signal Processing
Nonlinear Knowledge in Kernel Approximation
IEEE Transactions on Neural Networks
Support vector regression from simulation data and few experimental samples
Information Sciences: an International Journal
Cutting plane method for continuously constrained kernel-based regression
IEEE Transactions on Neural Networks
Multivariate convex support vector regression with semidefinite programming
Knowledge-Based Systems
Nonparametric bivariate copula estimation based on shape-restricted support vector regression
Knowledge-Based Systems
Support-vector modeling of electromechanical coupling for microwave filter tuning
International Journal of RF and Microwave Computer-Aided Engineering
Modeling financial dependence with support vector regression
Intelligent Data Analysis
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This paper explores the incorporation of prior knowledge in support vector regresion by the addition of constraints. Equality and inequality constraints are studied with the corresponding types of prior knowledge that can be considered for the method. These include particular points with known values, prior knowledge on any derivative of the function either provided by a prior model or available only at some specific points and bounds on the function or any derivative in a given domain. Moreover, a new method for the simultaneous approximation of multiple outputs linked by some prior knowledge is proposed. This method also allows consideration of different types of prior knowledge on single outputs while training on multiple outputs. Synthetic examples show that incorporating a wide variety of prior knowledge becomes easy, as it leads to linear programs, and helps to improve the approximation in difficult cases. The benefits of the method are finally shown on a real-life application, the estimation of in-cylinder residual gas fraction in spark ignition engines, which is representative of numerous situations met in engineering.