Yang, Liming; Wang, Laisheng - In: Advances in Data Analysis and Classification 7 (2013) 4, pp. 417-433
This paper investigate a class of semi-supervised support vector machines (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\text{ S }^3\mathrm{VMs}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mspace width="4.pt"/> <mtext>S</mtext> <msup> <mspace width="4.pt"/> <mn>3</mn> </msup> <mi mathvariant="normal">VMs</mi> </mrow> </math> </EquationSource> </InlineEquation>) with arbitrary norm. A general framework for the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$\text{ S }^3\mathrm{VMs}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mspace width="4.pt"/> <mtext>S</mtext> <msup> <mspace width="4.pt"/> <mn>3</mn> </msup> <mi mathvariant="normal">VMs</mi> </mrow> </math> </EquationSource> </InlineEquation> was first constructed based on a robust DC (Difference of Convex...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>
