The nature of statistical learning theory
The nature of statistical learning theory
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Large Scale Kernel Regression via Linear Programming
Machine Learning
Survival-Time Classification of Breast Cancer Patients
Computational Optimization and Applications
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Simpler knowledge-based support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
Incorporating prior knowledge in support vector regression
Machine Learning
A Multi-criteria Convex Quadratic Programming model for credit data analysis
Decision Support Systems
Incorporating prior model into Gaussian processes regression for WEDM process modeling
Expert Systems with Applications: An International Journal
A simple and effective method for incorporating advice into kernel methods
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Kernel regression with order preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Generative prior knowledge for discriminative classification
Journal of Artificial Intelligence Research
Cutting plane method for continuously constrained kernel-based regression
IEEE Transactions on Neural Networks
A group of knowledge-incorporated multiple criteria linear programming classifiers
Journal of Computational and Applied Mathematics
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Knowledge-Based multiclass support vector machines applied to vertical two-phase flow
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Rotational prior knowledge for SVMs
ECML'05 Proceedings of the 16th European conference on Machine Learning
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Peer-to-peer distributed text classifier learning in PADMINI
Statistical Analysis and Data Mining
Knowledge Bases Over Algebraic Models: Some Notes About Informational Equivalence
International Journal of Knowledge Management
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Prior knowledge, in the form of linear inequalities that need to be satisfied over multiple polyhedral sets, is incorporated into a function approximation generated by a linear combination of linear or nonlinear kernels. In addition, the approximation needs to satisfy conventional conditions such as having given exact or inexact function values at certain points. Determining such an approximation leads to a linear programming formulation. By using nonlinear kernels and mapping the prior polyhedral knowledge in the input space to one defined by the kernels, the prior knowledge translates into nonlinear inequalities in the original input space. Through a number of computational examples, including a real world breast cancer prognosis dataset, it is shown that prior knowledge can significantly improve function approximation.