Based　on　the　concepts　of　conlitron　and　multiconlitron,　we　propose　a　growing　construction　technique　for　improving　the　performance　of　piecewise　linear　classifiers　on　two-class　problems.　This　growing　technique　consists　of　two　basic　operations:　SQUEEZE　and　INFLATE,　which　can　produce　relatively　reliable　linear　boundaries.　In　the　convexly　separable　case,　the　growing　process,　forming　a　growing　support　conlitron　algorithm　(GSCA),　starts　with　an　initial　conlitron　and　uses　SQUEEZE　to　train　a　new　conlitron,　moving　its　classification　boundary　closer　to　the　interior　convex　region　and　fitting　the　data　distribution　better　statistically.　In　the　commonly　separable　case,　the　growing　process,　forming　a　growing　support　multiconlitron　algorithm　(GSMA),　starts　with　an　initial　multiconlitron　and　uses　INFLATE　and　SQUEEZE　to　train　a　new　multiconlitron,　making　its　classification　boundary　adjusted　to　improve　the　generalization　ability.　Experimental　evaluation　shows　that　the　growing　technique　can　simplify　the　structure　of　a　conlitron/multiconlitron　effectively　by　reducing　the　number　of　linear　functions,　largely　keeping　and　even　greatly　improving　the　level　of　classification　performances.　Therefore,　it　would　come　to　play　an　important　role　in　the　subsequent　development　of　piecewise　linear　learning,　with　the　main　goal　to　improve　piecewise　linear　classifiers　in　a　general　framework.　(C)　2014　Elsevier　B.V.　All　rights　reserved.