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https://www.researchgate.net/publication/2461310_Model_Selection_for_Support_Vector_Machines
Model selection in Support Vector machines is usually carried out by minimizing the quotient of the radius of the smallest enclosing sphere of the data and the observed margin on the training set.
https://www.sciencedirect.com/science/article/pii/S0925231203003758
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, model selection amounts to tuning kernel parameters and the slack penalty coefficient C.We begin by reviewing a recently developed probabilistic framework for SVM classification.Cited by: 209
https://papers.nips.cc/paper/1663-model-selection-for-support-vector-machines.pdf
Model Selection for Support Vector Machines 231 The paper is organized as follows. Section 2 describes the basics of SVMs, section 3 introduces a new functional based on the concept of the span of support vectors, section 4 considers the idea of rescaling data in feature space and section 5 …
https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html
Fitting a support vector machine¶ Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data. For the time being, we will use a linear kernel and set the C parameter to a very large number (we'll discuss the meaning of …
http://homepages.rpi.edu/~bennek/papers/bennett-bilevel07.pdf
In addition to model selection for support vector machines through continuous cross validation, the bilevel approach can also be applied to a wide variety of prob-lems like semi-supervised learning, predicting missing values in the data, kernel selection, multi-task learning and complexity minimization. This is …Cited by: 23
http://www.cenparmi.concordia.ca/ICFHR2008/Proceedings/papers/cr1099.pdf
3. Model selection with Support Vector Machines First, we discuss the principle of our approach, then we present kernels for generative models. These kernels are used to select component models in a mixture model defined according to Eq. (1). Finally we discuss the learning of prior weights in such a mixture model. c λi c wi 3.1. Principle
https://www.sciencedirect.com/science/article/pii/S0167947307000552
The problem of choosing a good parameter setting for a better generalization performance in a learning task is the so-called model selection. A nested uniform design (UD) methodology is proposed for efficient, robust and automatic model selection for support vector machines (SVMs).Cited by: 191
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