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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.
|
| Colección: | Machine intelligence and pattern recognition ;
v. 11. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Artificial neural networks and statistical pattern recognition : |b old and new connections / |c edited by Ishwar K. Sethi, Anil K. Jain. |
| 260 | |a Amsterdam ; |a New York : |b North-Holland ; |a New York, N.Y., U.S.A. : |b Elsevier Science Pub. Co. [distributor], |c 1991. | ||
| 300 | |a 1 online resource (xiv, 271 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Machine intelligence and pattern recognition ; |v v. 11 | |
| 504 | |a Includes bibliographical references and index. | ||
| 506 | |3 Use copy |f Restrictions unspecified |2 star |5 MiAaHDL | ||
| 533 | |a Electronic reproduction. |b [Place of publication not identified] : |c HathiTrust Digital Library, |d 2010. |5 MiAaHDL | ||
| 538 | |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |u http://purl.oclc.org/DLF/benchrepro0212 |5 MiAaHDL | ||
| 583 | 1 | |a digitized |c 2010 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 588 | 0 | |a Print version record. | |
| 520 | |a 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 continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the <Q>curse of dimensionality</Q> is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 546 | |a English. | ||
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 2 | |a Pattern Recognition, Automated |0 (DNLM)D010363 | |
| 650 | 2 | |a Neural Networks, Computer |0 (DNLM)D016571 | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) |0 (CaQQLa)201-0028094 | |
| 650 | 6 | |a R�eseaux neuronaux (Informatique) |0 (CaQQLa)201-0209597 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 650 | 7 | |a Pattern recognition systems. |2 fast |0 (OCoLC)fst01055266 | |
| 650 | 7 | |a Neuronales Netz |2 gnd |0 (DE-588)4226127-2 | |
| 650 | 7 | |a Aufsatzsammlung |2 gnd |0 (DE-588)4143413-4 | |
| 650 | 7 | |a Reconhecimento de padroes (engenharia eletrica) |2 larpcal | |
| 650 | 7 | |a Inteligencia artificial. |2 larpcal | |
| 650 | 0 | 7 | |a Mustererkennung. |2 swd |
| 653 | 0 | |a Artificial intelligence | |
| 700 | 1 | |a Sethi, Ishwar K., |d 1948- | |
| 700 | 1 | |a Jain, Anil K., |d 1948- | |
| 776 | 0 | 8 | |i Print version: |t Artificial neural networks and statistical pattern recognition. |d Amsterdam ; New York : North-Holland ; New York, N.Y., U.S.A. : Elsevier Science Pub. Co. [distributor], 1991 |w (DLC) 91024621 |w (OCoLC)24143926 |
| 830 | 0 | |a Machine intelligence and pattern recognition ; |v v. 11. | |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780444887405 |z Texto completo |