Benchmarking the n-tuple classifier with statlog dataset

The n-tuple recognition method was tested on 11 large real-world data sets and its performance compared to 23 other classification algorithms. On 7 of these, the results show no systematic performance gap between the n-tuple method and the others. Evidence was found to support a possible explanation for why the n-tuple method yields poor results for certain datasets. Preliminary empirical results of a study of the confidence interval (the difference between the two highest scores) are also reported. These suggest a counter-intuitive correlation between the confidence interval distribution and the overall classification performance of the system

An algebraic technique for the automatic recognition of visual patterns

Imperial Users onl

Good-turing estimation for the frequentist n-tuple classifier

We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system

Some aspects of adaptive logic for pattern recognition

This thesis deals with pattern recognizers (PRs) which are adaptive and amenable to hardware realization. Such PRs consist of networks of microcircuit modules (SLAMs: Stored-Logic Adaptive Microcircuits) which are used as feature extractors and which ensure a high throughput by their parallel operation. Previous workers have adopted the. number of training patterns as a measure for training such PRs on different pattern classes. In this thesis, the number of memory bits set in the SLAMs is considered instead, and this is shown to provide a better balance between sections of the PRs called discriminators. Simulations are carried out to observe how the size of the PR and the amount of training affect the performance and a quantitative comparison with a template matching classifier is presented. The effect of clustering the SLAM inputs within the input matrix is also investigated and PRs with SLAMs which have their outputs weighted according to their memory contents are also simulated. From the results, a technique to optimize the performance of the PRs is proposed and a possible development of the SLAM module is suggested

A comparison of selected pattern recognition functions

In this study computer simulation is used to compare selected pattern recognition functions. The Highleyman deck of 50 hand written characters provides one comparative data base. A second data base is derived from multispectral infrared sensor data taken over California's Imperial Valley. Emphasis is placed on comparing the classical minimum distance recognition functions with two new recognition functions introduced in a recent predecessor article.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22877/1/0000441.pd

Aspects of n-tuple character recognition for a blind reading aid

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis reports research conducted into a character recognition system suitable for use in a reading aid for the blind. A brief review of blind reading aids is given, showing the need for a device which is cheap, simple and effective. The structure of a proposed reading aid fulfilling these needs is outlined, with a list of the desired characteristics of each of its subsystems. The remainder of the thesis is concerned with research into just two of these subsystems: the input device and the character recognizer. A detailed review of pattern recognition by the n-tuple method is presented, followed by a description of the experimental techniques used in obtaining real data from a camera system, and in simulating various recognizer structures. The camera system and computer programs developed specifically for the research are described in detail. Several series of experiments are reported, concerned mainly with investigating problems associated directly with the blind reading aid, namely accommodation of multifont printed text and of the tracking errors inherent in data from a hand-held probe. A further series of experiments, aimed at improving the performance of the recognizer within fixed size constraints, i. e., optimisation, has a wider field of application. Finally suggestions are made as to how the recognizer might be implemented in a reading aid, using RAMs, ROMs, or PLAs as the main storage elements.Science Research Counci

A class of error tolerant pattern discrimination functions

A general pattern recognition problem is posed in Hilbert space. Two new solutions are then given and it is shown that the sensitivity of the pattern recognition functions to pattern perturbation can be a priori controlled. A series of examples demonstrate the principal results in a variety of settings.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23018/1/0000587.pd

Optical character recognition : use of OCR techniques in decentralized data collection for bibliographic information systems

Report of a feasibility study on the processing of AGRIS and INIS input in machine-readable form for decentralized data processing. Examines preparation of input in a form suitable for optical character recognition (OCR), electronic equipment, coding system

CPA handbook, volume 1;

https://egrove.olemiss.edu/aicpa_guides/1107/thumbnail.jp