CLASSIFICATION BY VARYING FEATURES WITH AN ERRING SENSOR

被引：1

作者：

GUTMAN, PO

PELEG, K

BENHANAN, U

机构：

[1] Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Haifa

来源：

AUTOMATICA | 1994年 / 30卷 / 12期

关键词：

BAYES METHODS; CLASSIFICATION; DECISION THEORY; SENSORS; SIGNAL DETECTION;

D O I：

10.1016/0005-1098(94)90054-X

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A method is proposed for unsupervised classification by a feature that may vary with time, measured by an erring sensor. A classification threshold for the erring sensor is found such that the misclassification is minimized. It is shown that the method is an application of Bayes rule without knowledge of the a priori probabilities, while estimating the class conditional probabilities by an erring sensor model. Sorting of fruits is presented as an illustrative example.

引用

页码：1943 / 1948

页数：6

共 19 条

[1] CLASSIFICATION OF FRUITS BY A BOLTZMANN PERCEPTRON NEURAL NETWORK [J].