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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Journal ArticleDOI
Study on the wire feed speed prediction of double-wire-pulsed MIG welding based on support vector machine regression
Ping Yao,Jiaxiang Xue,Kang Zhou +2 more
TL;DR: Wang et al. as mentioned in this paper analyzed ten factors which affect the wire feed speed in reality and conducted corresponding correlation analysis, which can optimize the doublewire welding technological designing process and improve the intelligent double-wire welding industry.
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Prediction of chemical carcinogenicity by machine learning approaches
TL;DR: A successful application of machine learning approaches to the prediction of chemical carcinogenicity from molecular structure descriptors and results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogensicity of compounds.
Book ChapterDOI
Model Selection for Support Vector Classifiers via Genetic Algorithms. An Application to Medical Decision Support
TL;DR: A Genetic Algorithm-based wrapper, which seeks to evolve hyperparameter values using an empirical error estimate as a fitness function, is proposed and experimentally evaluated on a medical dataset, producing satisfactory results.
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A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather
TL;DR: In this paper, the authors explore the relationship between weather and human emotion through a cloud-based Big Data solution and provide a practical demonstration of how Big Data technologies and infrastructures can be developed and delivered where nuances and correlations between combinations of large-scale and heterogeneous data can be discovered.
Book ChapterDOI
A comparison of model selection methods for multi-class support vector machines
Huaqing Li,Feihu Qi,Shaoyu Wang +2 more
TL;DR: The two methods for multi-class SVMs with the one-against-one strategy are compared and their properties are discussed and their performance is analyzed based on experimental results.
References
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