title: Designing prognostic models by reinforcing linear separation.
abstract: Variety of prognostic models can be designed on the basis of learning sets by using the principle of linear separability. The degree of linear separability of two learning sets can be evaluated on the basis of the minimal value of the perceptron criterion function, which belongs to a larger family of the convex and piecewise linear (CPL) criterion functions. Parameters constituting the minimal value of a given CPL criterion function can define particular prognostic model. Prognostic models have been designed this way, for example, on the basis of genetic data sets.
authors.affiliation: Computer Science Department, Białystok University of Technology, Warsaw, Poland. lbobrowski@ibib.waw.pl
authors.forename: Leon
authors.initials: L
authors.lastname: Bobrowski
mesh_terms: D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D016014:Linear Models; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011379:Prognosis