Sparse solution of least-squares twin multi-class support vector machine using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4265" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4275" altimg="si52.svg"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norm for classification and feature selection
Hossein Moosaei, Milan Hladík
Topics & Concepts
Support vector machineComputer scienceStructural risk minimizationFeature selectionArtificial intelligenceMachine learningClassifier (UML)AlgorithmMinificationProgramming languageFace and Expression RecognitionSpectroscopy and Chemometric AnalysesMachine Learning and ELM