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Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
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2010
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Inseparable ProblemsSupport Vector MachineEngineeringMachine LearningData ScienceData MiningPattern RecognitionComputational Learning TheoryReproducing Kernel MethodKnowledge DiscoveryRandom MappingKernel TechniquesComputer ScienceLinear SvmDimensionality ReductionSupervised LearningKernel MethodHigh Dimensional Space
Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is often time consumi...