Designing Prognostic Models by Reinforcing Linear Separation

机译：通过加强线性分离来设计预测模型

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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.

机译：可以使用线性可分离性原理，在学习集的基础上设计各种预测模型。可以基于感知器标准函数的最小值来评估两个学习集的线性可分离程度，感知器标准函数属于凸和分段线性（CPL）标准函数的较大族。构成给定CPL标准函数最小值的参数可以定义特定的预后模型。已经以这种方式设计了预后模型，例如，基于遗传数据集。

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来源
《Data and knowledge for medical decision support》|2013年|36-40|共5页
会议地点 Prague(CZ)
作者
Leon BOBROWSKI;
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作者单位

Computer Science Department, Bialystok University of Technology, Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Linear prognostic models; CPL criterion function; feature selection;

机译：线性预测模型； CPL标准功能；功能选择;

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4. Designing Prognostic Models by Reinforcing Linear Separation [C] . Leon BOBROWSKI The Special Topics Conference of the European Federation for Medical Informatics . . 2013

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5. Nonlinear stochastic modeling and analysis of cardiovascular system dynamics - Diagnostic and prognostic applications. [D] . Yang, Hui. 2009

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Designing Prognostic Models by Reinforcing Linear Separation

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