Statistical and Neural Classifiers: An Integrated Approach to DesignSpringer Science & Business Media, 2012-12-06 - 295 psl. Automatic (machine) recognition, description, classification, and groupings of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. Given a pattern, its recognition/classification may consist of one of the following two tasks: (1) supervised classification (also called discriminant analysis); the input pattern is assigned to one of several predefined classes, (2) unsupervised classification (also called clustering); no pattern classes are defined a priori and patterns are grouped into clusters based on their similarity. Interest in the area of pattern recognition has been renewed recently due to emerging applications which are not only challenging but also computationally more demanding (e. g. , bioinformatics, data mining, document classification, and multimedia database retrieval). 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 received increased attention. Neural networks and statistical pattern recognition are two closely related disciplines which share several common research issues. Neural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation. On the other hand, neural networks can derive benefit from some well-known results in statistical pattern recognition. |
Turinys
Taxonomy of Pattern Classification Algorithms | 27 |
Performance and the Generalisation Error | 76 |
Neural Network Classifiers | 135 |
Integration of Statistical and Neural Approaches | 191 |
Model Selection | 209 |
Appendices | 253 |
References | 267 |
| 287 | |
Kiti leidimai - Peržiūrėti viską
Statistical and Neural Classifiers– An Integrated Approach to Design Sarunas Raudys Ribota peržiūra - 2001 |
Statistical and Neural Classifiers– An Integrated Approach to Design Sarunas Raudys Peržiūra negalima - 2014 |
Pagrindiniai terminai ir frazės
activation function approach artificial neural networks asymptotic error Bayes bi-variate classification algorithms classification error classification rule classifier design complexity cost function curve data model decision boundary decision tree density function design set dimensionality distribution density Equation error rate Euclidean distance Euclidean distance classifier example feature space Figure Fisher classifier Fisher linear DF generalisation error hidden layer increase initialisation learning step linear classifier linear discriminant function M₁ M₂ Mahalanobis distance mean vectors method minimise multivariate N₁ N₂ neural networks neurones non-linear nonparametric number of features number of iterations number of training obtain P₂ Parzen window pattern classes pattern recognition performance probability of misclassification problem quadratic classifiers random variable Raudys regularisation sample covariance matrix Section selection SLP classifier spherical Gaussian statistical classifiers subset targets training process training sets training vectors transformation utilised validation set values variance weight vector zero