Choosing Multiple Parameters for Support Vector Machines
TLDR
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.Abstract:
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.read more
Citations
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Support vector machines : A recent method for classification in chemometrics
TL;DR: Support Vector Machines are a new generation of classification method that attempts to produce boundaries between classes by both minimising the empirical error from the training set and also controlling the complexity of the decision boundary, which can be non-linear.
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Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization
TL;DR: An ant colony algorithm for synchronous feature selection and parameter optimization for support vector machine in intelligent fault diagnosis of rotating machinery is presented and the advantages of the proposed method are evaluated.
Journal ArticleDOI
New results on error correcting output codes of kernel machines
TL;DR: A new decoding function is introduced that combines the margins through an estimate of their class conditional probabilities, which can be used to tune kernel hyperparameters and empirical evaluations on model selection indicate that the bound leads to good estimates of kernel parameters.
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Short-term load forecasting using a kernel-based support vector regression combination model
JinXing Che,Jianzhou Wang +1 more
TL;DR: The proposed combination model provides a new way to kernel function selection of SVR model by using a novel individual model selection algorithm and increases electric load forecasting accuracy compared to the best individual kernel-based SVR models.
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Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
TL;DR: All machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms.
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