Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Handwriting Recognition Using Local Methods for Normalization and Global Methods for Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A tutorial on support vector regression
Statistics and Computing
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
Support vector regression to predict porosity and permeability: Effect of sample size
Computers & Geosciences
Support vector machine for multi-classification of mineral prospectivity areas
Computers & Geosciences
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In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. Beyond simple regression, neural networks have been used to develop more accurate porosity correlations. Unfortunately, neural network-based correlations have limited generalization ability and global correlations for a field are usually less accurate compared to local correlations for a sub-region of the reservoir. In this paper, support vector machines are explored as an intelligent technique to correlate porosity to well log data. Recently, support vector regression (SVR), based on the statistical learning theory, have been proposed as a new intelligence technique for both prediction and classification tasks. The underlying formulation of support vector machines embodies the structural risk minimization (SRM) principle which has been shown to be superior to the traditional empirical risk minimization (ERM) principle employed by conventional neural networks and classical statistical methods. This new formulation uses margin-based loss functions to control model complexity independently of the dimensionality of the input space, and kernel functions to project the estimation problem to a higher dimensional space, which enables the solution of more complex nonlinear problem optimization methods to exist for a globally optimal solution. SRM minimizes an upper bound on the expected risk using a margin-based loss function (@e-insensitivity loss function for regression) in contrast to ERM which minimizes the error on the training data. Unlike classical learning methods, SRM, indexed by margin-based loss function, can also control model complexity independent of dimensionality. The SRM inductive principle is designed for statistical estimation with finite data where the ERM inductive principle provides the optimal solution (the empirical risk approaches the expected risk) only for asymptotic (large sample data). The SRM principle matches model complexity to the available data through controlling the tradeoff between complexity of the model and quality of fitting the data. It is this difference which equips support vector machines (SVM) with a greater ability to generalize beyond the training data. Here, a SVR-based porosity prediction model is developed for a heterogeneous sandstone reservoir. The SVR method has been compared to multilayer perceptron, General Regression Neural Networks, and Radial Basis Function Neural Networks. The results reveal that the SVR method exhibits superior accuracy and robustness with respect to these neural network methods especially with respect to accuracy when generalizing to previously unseen porosity data.