Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
A sparse representation for function approximation
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Network Performance Assessment for Neurofuzzy Data Modelling
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
A Unified Framework for Regularization Networks and Support Vector Machines
A Unified Framework for Regularization Networks and Support Vector Machines
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
Adapting Kernels by Variational Approach in SVM
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Leave-One-Out Bounds for Support Vector Regression Model Selection
Neural Computation
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
An approach for kernel selection based on data distribution
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Feature selection for support vector regression using probabilistic prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Neurocomputing
Probabilistic support vector machines for classification of noise affected data
Information Sciences: an International Journal
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
On-line Support Vector Regression of the transition model for the Kalman filter
Image and Vision Computing
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In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. This paper concentrates on the derivation of the evidence and error bar approximation for regression problems. An error bar formula is derived based on the ε-insensitive loss function.