The Accelerated Relaxation Method for Linear Inequalities
IEEE Transactions on Computers
Solution of Linear Inequalities
IEEE Transactions on Computers
Decision-Directed Estimation of a Two-Class Decision Boundary
IEEE Transactions on Computers
Comment on "Pattern Classification Design by Linear Programming"
IEEE Transactions on Computers
An Algorithm for the Solution of Linear Inequalities
IEEE Transactions on Computers
An Algorithm for Pattern Classification Using Eigenvectors
IEEE Transactions on Computers
Design of Multicategory Pattern Classifiers with Two-Category Classifier Design Procedures
IEEE Transactions on Computers
On a Method of Sequential Pattern Recognition
IEEE Transactions on Computers
IEEE Transactions on Computers
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using Supervised Complexity Measures in the Analysis of Cancer Gene Expression Data Sets
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Information Sciences: an International Journal
Peer-to-peer distributed text classifier learning in PADMINI
Statistical Analysis and Data Mining
Analysis of data complexity measures for classification
Expert Systems with Applications: An International Journal
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Abstract A common nonparametric method for designing linear discriminant functions for pattern classification is the iterative, or "adaptive," weight adjustment procedure, which designs the discriminant function to do well on a set of typical patterns. This paper presents a linear programming formulation of discriminant function design which minimizes the same objective function as the "fixed-increment" adaptive method. With this formulation, as with the adaptive methods, weights which tend to minimize the number of classification errors are computed for both separable and nonseparable pattern sets, and not just for separable pattern sets as has been the emphasis in previous linear programming formulations.