New Support Vector Algorithms
TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
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A study on SMO-type decomposition methods for support vector machines
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Support vector machines and its applications in chemistry
TL;DR: Support vector machines (SVMs) are a promising machine learning method originally developed for pattern recognition problem based on structural risk minimization as discussed by the authors, which can be divided into two categories: support vector classification (SVC) machines and support vector regression (SVR) machines.
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Training v -support vector regression: theory and algorithms
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: This work discusses the relation between-support vector regression (-SVR) and v- support vector regression (v-SVR), and focuses on properties that are different from those of C- Support vector classification (C-SVC) andv-supportvector classification (v -SVC).
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Predicting motor vehicle crashes using Support Vector Machine models.
TL;DR: In this article, Support Vector Machine (SVM) models were used for predicting motor vehicle crashes. But, the results showed that SVM models do not overfit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research.
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A support vector machine-based ensemble algorithm for breast cancer diagnosis
TL;DR: The proposed WAUCE model achieves a higher accuracy with a significantly lower variance for breast cancer diagnosis compared to five other ensemble mechanisms and two common ensemble models, i.e., adaptive boosting and bagging classification tree.
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