Linear Ranked Regression - Designing Principles

Linear Ranked Regression - Designing Principles,10.1007/3-540-32390-2_10,Leon Bobrowski

Linear Ranked Regression - Designing Principles  
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A priori information about selected pattern recognition problem is often a necessary precondition to reach a sufficient quality of the problem solution. Such information could take the form of the ranked order in the referencing sets of objects or events. For example, we can encounter a case when one patient is suffering from a more advanced stage of a disease than the another one. In other cases, we can assume that some events took place earlier or later than the regarded one. A ranked regression task is aimed at designing such linear transformation of multivariate data sets on the line which preserves with the highest precision possible the ranked order. The convex and piecewise linear (CPL) criterion functions are used here for designing ranked linear models.
Conference: Computer Recognition Systems - CORES , pp. 105-112, 2005
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