Some experimental results on placement techniques
Maurice Hanan, Peter K. Wolff, et al.
DAC 1976
A nonparametric sequential pathyutern classifier called a linear sequential classifier (LSC) is presented. The pattern_components are measured sequentially and the decisions either to measure the next component or to stop and classify the pattern are made using linear functions derived from sample patterns based on the least mean-square error criterion. The required linear functions are computed using an adaptlon of GrevilJe's recursive algorithm for computing the generalized inverse of a matrix. A recursive algorithm for computing the least mean-square error is given and is used to determine the order in which the pattern components are measured. Under the assumption of two equiprobable classes that are normally distributed with equal covariance matrices, it is shown that the LSC is equivalent to Wald's sequential probability ratio test. Computer-simulated experiments indicate that the LSC is more effective than existing nonparametric sequential classifiers. © 1972, IEEE. All rights reserved.
Maurice Hanan, Peter K. Wolff, et al.
DAC 1976
Israel Cidon, Leonidas Georgiadis, et al.
IEEE/ACM Transactions on Networking
Qing Li, Zhigang Deng, et al.
IEEE T-MI
David A. Selby
IBM J. Res. Dev