As　a　special　form　of　piecewise　linear　classifier,　the　convex　polyhedron　classifier　is　simple　to　implement　and　achieves　rapid　response　in　real-time　classification.　However,　it　usually　performs　badly　in　the　case　of　high　noise　where　severe　boundary　intrusion　exists.　Inspired　by　the　scheme　of　soft　margin　in　support　vector　machine,　in　this　paper　we　propose　a　soft-margin　convex　polyhedron　classifier　for　nonlinear　classification　task.　The　base　(linear)　classifier　is　first　generalized　to　its　soft-margin　version　through　kernelization　process　and　slack　variables.　In　each　local　region,　the　soft-margin　base　classifier　learns　a　decision　hyperplane　with　noise　tolerance.　Then,　a　series　of　learned　hyperplanes　are　structurally　integrated　into　a　convex　polyhedron　classifier,　which　is　essentially　a　convex　polyhedron　that　encloses　one　class　and　excludes　the　other　class　outside.　Experimental　results　on　fifteen　benchmark　datasets　show　the　proposed　soft-margin　convex　polyhedron　classifier　is　comparable　to　linear　support　vector　machine　and　four　piecewise　linear　classifiers,　but　does　not　perform　as　well　as　the　support　vector　machine　with　radial　basis　function　kernel　in　general.　When　random　noises　are　added　to　datasets,　the　soft-margin　convex　polyhedron　classifier　achieves　similar　or　better　accuracies　with　the　well-known　classifiers　used　for　comparison,　implying　its　promising　ability　of　noise　tolerance.