On an optimal linear pattern classification procedure

Computer Science and Engineering, 2011

The objective of this paper is to discuss and compare some aspect of pattern recognition, among the various framework in which pattern recognition has been traditional formulated. The primary goal of pattern recognition is supervised or unsupervised classification. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation.

Journal of information and communication convergence engineering

In this work, we present solutions of two problems. First, the representation of pattern recognition problem in the standard nml T table of the algorithm estimate calculation was considered. Second, the problem of finding the model, consisting of the optimal parameters of an algorithm was considered. Such procedure is carried out by the selection optimal values of the parameters of extreme algorithms. This serves to reduce the number of calculations in the algorithms of estimate calculation and to increase the quality of recognition process. The algorithmic data base of the developed system was based on mathematical apparatus of pattern recognition.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

AbstractÐThe primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

In this work it is presented a methodology for the development of a pattern recognition system using classification methods as discriminant analysis and artificial neural networks. In this methodology, the statistical analysis is contemplated, with the purpose of retaining the observations and the important characteristics that can produce an appropriate classification, and allows, as well, to detect outliers’ observations, multicolinearity between variables, among other things.

Management Science, 1972

Emerging Technologies in Data Mining and Information Security

This study aims to implement the following four advanced pattern recognition algorithms, such as "optimal Bayesian classifier," "anti-Bayesian classifier," "decision trees (DTs)," and "dependence trees (DepTs)" on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the fourth section, a comparison between the classification accuracy of DT and DepT classification methods for both the artificial and real-life datasets is discussed. In the fifth section, a comparison between the classification accuracy of the four algorithms, such as (a) Bayes, (b) anti-Bayes, (c) DTs, and (d) DepTs for both the artificial and real datasets is explained. We used 5fold cross-validation to determine the classification accuracy of individual, machine learning-based, advanced pattern recognition (PR) models.

2002

In this paper, we propose a method for multi-class pattern classification by combined use of Multinomial Logit Model (MLM) and K-nearest neighbor rule (K-NN). Multinomial Logit Model (MLM) is one of the neural network models for multi-class pattern classification, and is supposed to be equal or better in class&cation performance than linear classification methods. K-NN is a simple but powerful non-parametric classification tool whose error probability does not exceed double of b y e s error. However, it is also known that such high performance of K-NN is not always expected if number of dimension of input feature vector space is large. Therefore, first we train MLM using the training vectors, and then apply K-NN to the output of the MLM. By this, since K-NN is applied to the compressed low dimension vectors, it is expected not only to bring out natural performance of K-NN but also to shorten computation time.

This paper proposes an efficient three-stage classifier for handwritten digit recognition based on NN (Neural Network) and SVM (Support Vector Machine) classifiers. The classification is performed by 2 NNs and one SVM. The first NN is designed to provide a low misclassification rate using a strong rejection criterion. It is applied on a small set of easy to extract features. Rejected patterns are forwarded to the second NN that uses additional, more complex features, and utilizes a wellbalanced rejection criterion. Finally, rejected patterns from the second NN are forwarded to an optimized SVM that considers only the “top k” classes as ranked by the NN. This way a very fast SVM classification is obtained without sacrificing the classifier accuracy. The obtained recognition rate is among the best on the MNIST database and the classification time is much better compared to the single SVM applied on the same feature set.

Pattern Recognition Letters, 2001

Multiple classi®er systems (MCSs) based on the combination of outputs of a set of dierent classi®ers have been proposed in the ®eld of pattern recognition as a method for the development of high performance classi®cation systems. Previous work clearly showed that multiple classi®er systems are eective only if the classi®ers forming them are accurate and make dierent errors. Therefore, the fundamental need for methods aimed to design``accurate and diverse'' classi®ers is currently acknowledged. In this paper, an approach to the automatic design of multiple classi®er systems is proposed. Given an initial large set of classi®ers, our approach is aimed at selecting the subset made up of the most accurate and diverse classi®ers. A proof of the optimality of the proposed design approach is given. Reported results on the classi®cation of multisensor remote sensing images show that this approach allows the design of eective multiple classi®er systems. Ó

1992

Difficult pattern recognition problems involving large class sets and noisy input can be solved by a multiple classifier system, which allows simultaneous use of arbitrary feature descriptors and classification procedures. Independent decisions by each classifier can be combined by methods of the highest rank, Borda count, and logistic regression, resulting in substantial improvement in overall correctness