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Artificial neural networks and statistical pattern recognition : old and new connections /
With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are cont...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; New York : New York, N.Y., U.S.A. :
North-Holland ; Elsevier Science Pub. Co. [distributor],
1991.
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| Colección: | Machine intelligence and pattern recognition ;
v. 11. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections; Copyright Page; FOREWORD; PREFACE; Table of Contents; PART 1: ANN AND SPR RELATIONSHIP; CHAPTER 1. EVALUATION OF A CLASS OF PATTERN-RECOGNITION NETWORKS; INTRODUCTION; 1. A CLASS OF PATTERN-RECOGNITION NETWORKS; 2. A REPRESENTATION OF THE JOINT DISTRIBUTION; 3. A CLASS OF CLASSIFICATION FUNCTIONS; 4. DETERMINATION OF COEFFICIENTS FROM SAMPLES; 5. SOME COMMENTS ON COMPARING DESIGN PROCEDURES; 6. SOME COMMENTS ON THE CHOICE OF OBSERVABLES, AND ON INVARIANCE PROPERTIES; ACKNOWLEDGMENT
- 5. PEAKING IN THE CLASSIFICATION PERFORMANCE WITH INCREASE IN DIMENSIONALITY6. EFFECT OF THE NUMBER OF NEURONS IN THE HIDDEN LAYERON THE PERFORMANCE OF ANN CLASSIFIERS; 7. DISCUSSION; References; CHAPTER 4. On Tree Structured Classifiers; Abstract; 1. INTRODUCTION; 2. DECISION RULES AND CLASSIFICATION TREES; 3. CLASSIFICATION TREE CONSTRUCTION AND ERROR RATE ESTIMATION; 4. TREE PRUNING ALGORITHMS; 5. EXPERIMENTAL RESULTS; 6. CONCLUSION; REFERENCES; CHAPTER 5. Decision tree performance enhancement using an artificial neural network implementation; Abstract; 1. INTRODUCTION
- 2. DECISION TREE CLASSIFIER ISSUES3. MULTILAYER PERCEPTRON NETWORKS; 4. AN MLP IMPLEMENTATION OF TREE CLASSIFIERS; 5. TRAINING THE TREE MAPPED NETWORK; 6. PERFORMANCE EVALUATION; 7. CONCLUSIONS; REFERENCES; PART 2: APPLICATIONS; CHAPTER 6. Bayesian and neural network pattern recognition : atheoretical connection and empirical results with handwritten characters; Abstract; 1 Introduction; 2 Bayes Classifier; 3 Artificial Neural Networks and Back Propagation; 4 Relationship; 5 Experimental Results; 6 Discussion; 7 Conclusion; 8 Acknowledgements; References
- CHAPTER 7. Shape and Texture Recognition by a Neural Network1. INTRODUCTION; 2. ZERNIKE MOMENT FEATURES FOR SHAPE RECOGNITION; 3. RANDOM FIELD FEATURES FOR TEXTURE RECOGNITION; 4. MULTI-LAYER PERCEPTRON CLASSIFIER; 5. CONVENTIONAL STATISTICAL CLASSIFIERS; 6. EXPERIMENTAL STUDY ON SHAPE CLASSIFICATION; 7. EXPERIMENTAL STUDY ON TEXTURE CLASSIFICATION; 8. DISCUSSIONS AND CONCLUSIONS; 9. REFERENCES; CHAPTER 8. Neural Networks for Textured Image Processing; Abstract; 1. INTRODUCTION; 2. DETECTION OF EDGES IN COMPUTER AND HUMAN VISION; 3. TEXTURE ANALYSIS USING MULTIPLE CHANNEL FILTERS