Showing papers on "Linear classifier published in 1981"

Bayesian classification and classification performance for independent distributions (Corresp.)

Abstract: Some general conditions are derived for the mean recognition accuracy of a Bayesian classifier to approach unity as the feature dimensionality increases to infinity.

Analysis of pattern recognition by man using detection experiments.

Abstract: This paper addresses the problem of analyzing biological pattern recognition systems. As no complete analysis is possible due to limited observability, the theoretical part of the paper examines some principles of construction for recognition systems. The relations between measurable and characteristic variables of these systems are described. The results of the study are: 1. Human recognition systems can always be described by a model consisting of an analyzer (FA) and a linear classifier. 2. The linearity of the classifier places no limits on the universal validity of the model. The principle of organization of such a system may be put into effect in many different ways. 3. The analyzer function FA determines the transformation of external patterns into their internal representations. For the experiments described in this paper, FA can be approximated by a filtering operation and a transformation of features (contour line filter). 4. Narrow band filtering (comb filter) in the space frequency domain is inadequate for pattern recognition because noise of different bandwidths and mean frequencies affects sinusoidal gratings differently. This excludes the use of a Fourier analyzer. 5. The relations between the measurable variables, which are the probabilities of detection (PD curves), and the characteristic variables of the recognition system are established analytically. 6. The probability of detection not only depends on signal energy but also on signal structure. This would not be the case in a simple matched filter system. 7. The differing probabilities of error in multiple detection experiments show that the interference is pattern specific and the bandwidth (steepness of the PD curves) is different for the different sets of patterns. 8. The distance between the reference vectors in feature space can be determined from the internal representation of the patterns defined by the model. Through multiple detection experiments it is possible to determine not only the relative distances between the patterns but also their absolute position in feature space.

Signal classification for automatic industrial inspection

Abstract: Details of a system designed to inspect web-type products are presented. The sensing device used is a laser scanner, and a brief description of the scanner is given. A set of bandpass filters is proposed as feature extracters. The required characteristics of the filters are determined through digital simulation, using feature selection methods. The linear classifier is designed from a set of training signals. Contextual information is used in the classification of signals into good product or into various defective categories. In addition, results of a study on inspection of magnetic tapes and abrasive sheets are described.

Optical recognition of statistical patterns

Abstract: Optical implementation of the Fukunaga-Koontz transform (FKT) and the Least-Squares Linear Mapping Technique (LSLMT) is described. The FKT is a linear transformation which performs image feature extraction for a two-class image classification problem. The LSLMT performs a transform from large dimensional feature space to small dimensional decision space for separating multiple image classes by maximizing the interclass differences while minimizing the intraclass variations. The FKT and the LSLMT were optically implemented by utilizing a coded phase optical processor. The transform was used for classifying birds and fish. After the F-K basis functions were calculated, those most useful for classification were incorporated into a computer generated hologram. The output of the optical processor, consisting of the squared magnitude of the F-K coefficients, was detected by a T.V. camera, digitized, and fed into a micro-computer for classification. A simple linear classifier based on only two F-K coefficients was able to separate the images into two classes, indicating that the F-K transform had chosen good features. Two advantages of optically implementing the FKT and LSLMT are parallel and real time processing.

Linear Classifiers for Nonseparable Classes

Abstract: In this chapter we describe several training procedures based on the gradient and stochastic approximation techniques of the preceding chapter. Although our discussion in this chapter is restricted to two-class cases, the techniques may be extended to multiple-class cases by using the concepts of Section 2.6. The training procedures of this chapter apply to pairs of classes that are linearly nonseparable, i.e., that are not linearly separable, as well as those that are linearly separable. Linearly nonseparable classes include pairs of classes that are separable but not by a hyperplane, as well as pairs of classes that overlap.