Journal ArticleDOI
Experiments with Highleyman's Data
J.H. Munson,R.O. Duda,P.E. Hart +2 more
TLDR
Nearest-neighbor classification is used to explain the high error rates obtained by general statistical procedures, and the minimum human error rate is estimated, and suggested as a performance standard.Abstract:
—The results of three experiments with Highleyman's hand-printed characters are reported. Nearest-neighbor classification is used to explain the high error rates (42 to 60 percent) obtained by general statistical procedures. An error rate of 32 percent is obtained by preceding piecewise-linear classification by edge-detecting preprocessing. The minimum human error rate is estimated, and suggested as a performance standard.read more
Citations
More filters
Journal ArticleDOI
Bibliography on estimation of misclassification
TL;DR: Articles, books, and technical reports on the theoretical and experimental estimation of probability of misclassification are listed for the case of correctly labeled or preclassified training data.
Journal ArticleDOI
State of the art in pattern recognition
TL;DR: This paper reviews statistical, adaptive, and heuristic techniques used in laboratory investigations of pattern recognition problems and includes correlation methods, discriminant analysis, maximum likelihood decisions minimax techniques, perceptron-like algorithms, feature extraction, preprocessing, clustering and nonsupervised learning.
Journal ArticleDOI
Automatic recognition of handprinted characters—The state of the art
Ching Y. Suen,M. Berthod,S. Mori +2 more
TL;DR: Recognition algorithms, data bases, character models, and handprint standards are examined and Achievements in the recognition of handprinted numerals, alphanumerics, Fortran, and Katakana characters are analyzed and compared.
Journal ArticleDOI
On pattern classification algorithms--Introduction and survey
Yu-Chi Ho,Ashok K. Agrawala +1 more
TL;DR: It is shown that these algorithms can be classified according to the type of input information required and that the techniques of estimation, decision, and optimization theory can be used effectively to derive known as well as new results.
Journal ArticleDOI
On pattern classification algorithms introduction and survey
Yu-Chi Ho,Ashok K. Agrawala +1 more
TL;DR: It is shown that these algorithms can be classified according to the type of input information required and that the techniques of estimation, decision, and optimization theory can be used to effectively derive known as well as new results.
References
More filters
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Journal ArticleDOI
Pattern Classification by Iteratively Determined Linear and Piecewise Linear Discriminant Functions
R. O. Duda,H. Fossum +1 more
TL;DR: This paper describes iterative procedures for determining linear and piecewise linear discriminant functions for multicategory pattern classifiers and shows that this approach compares favorably with other classification methods.
Journal ArticleDOI
Linear Decision Functions, with Application to Pattern Recognition
TL;DR: This paper is concerned with the study of a particular class of categorizers, the linear decision function, which can be empirically designed without making any assumptions whatsoever about either the distribution of the receptor measurements or the a priori probabilities of occurrence of the pattern classes, providing an appropriate pattern source is available.
Journal ArticleDOI
A Recognition Method Using Neighbor Dependence
TL;DR: A nearest-neighbor dependence method is obtained by going beyond the usual assumption of statistical independence, and the effect of neighbor dependence upon recognition performance is significant.
ReportDOI
Graphical-data-processing research study and experimental investigation
Peter E. Hart,John H. Munson +1 more
TL;DR: The automatic training procedure of the 1,000-image optical preprocessor, which is a significant technical development of MINOS II is analyzed in detail; both logical and electrical circuits are given.