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Model selection of SVMs using GA approach
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1016/s0925-2312(02)00601-xopenalex-snapshot
Evaluation of simple performance measures for tuning SVM hyperparameters
inferredcites →https://doi.org/10.1016/s0925-2312(03)00375-8openalex-snapshot
Model selection for support vector machine classification
inferredcites →https://doi.org/10.1023/a:1012427100071openalex-snapshot
A Simple Decomposition Method for Support Vector Machines
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Choosing Multiple Parameters for Support Vector Machines
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Genetic Algorithms and Engineering Design
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Comparing support vector machines with Gaussian kernels to radial basis function classifiers
inferredcites →https://doi.org/10.1109/tnn.2002.1031955openalex-snapshot
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
inferredcites →https://doi.org/10.1162/089976600300015042openalex-snapshot
Bounds on Error Expectation for Support Vector Machines
inferredcites →https://doi.org/10.17877/de290r-14262openalex-snapshot
Making Large-Scale SVM Learning Practical
inferredcites →https://doi.org/10.2307/1269742openalex-snapshot
Machine Learning, Neural and Statistical Classification
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems
inferredcites →https://doi.org/10.7551/mitpress/1113.003.0022openalex-snapshot
GACV for Support Vector Machines
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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