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  • 1
    Online Resource
    Online Resource
    Advances in Machine Learning and Cybernetics : 4th International Conference, ICMLC 2005, Guangzhou, China, August 18-21, 2005, Revised Selected Papers. (2006)
    Berlin, Heidelberg :Springer Berlin / Heidelberg,
    Details
    Keywords: Machine learning -- Congresses. ; Cybernetics -- Congresses. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (1128 pages)
    Edition: 1st ed.
    ISBN: 9783540335856
    Series Statement: Lecture Notes in Computer Science Series ; v.3930
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=3067690
    DDC: 006.31
    Language: English
    Note: Intro -- Preface -- Organization -- Table of Contents -- Author Index.
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  • 2
    Online Resource
    Online Resource
    Soft Computing in Case Based Reasoning. (2000)
    London :Springer London, Limited,
    Details
    Keywords: Soft computing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (379 pages)
    Edition: 1st ed.
    ISBN: 9781447106876
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=3074255
    Language: English
    Note: Soft Computing in Case Based Reasoning -- Copyright -- Foreword -- Contents -- List of Contributors -- 1. A Tutorial on Case Based Reasoning -- 2. On the Notion of Similarity in Case Based Reasoning and Fuzzy Theory -- 3. Formalizing Case Based Inference Using Fuzzy Rules -- 4. Hybrid Approaches for Integrating Neural Networks and Case Based Reasoning: From Loosely Coupled to Tightly Coupled Models -- 5. Towards Integration of Memory Based Learning and Neural Networks -- 6. A Genetic Algorithm and Growing Cell Structure Approach to Learning Case Retrieval Structures -- 7. An Architecture for Hybrid Creative Reasoning -- 8. Teacher: A Genetics Based System for Learningand Generalizing Heuristics -- 9. Fuzzy Logic Based Neural Network for Case Based Reasoning -- 10. Case Based Systems: A Neuro-Fuzzy Method for Selecting Cases -- 11. Neuro-Fuzzy Approach for Maintaining Case Bases -- 12. A Neuro-Fuzzy Methodology for Case Retrieval and an Object-Oriented Case Schema for Structuring Case Bases and their Application to Fashion Footwear Design -- 13. Adaptation of Cases for Case Based Forecasting with Neural Network Support -- 14. Armchair Mission to Mars: Using Case Based Reasoning and Fuzzy Logic to Simulate a Time Series Model of Astronaut Crews -- 15. Applications of Soft CBR at General Electric -- Index -- About the Editors.
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  • 3
    Online Resource
    Online Resource
    Sensitivity Analysis for Neural Networks. (2009)
    Berlin, Heidelberg :Springer Berlin / Heidelberg,
    Details
    Keywords: Neural circuitry. ; Electronic books.
    Description / Table of Contents: This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perception neural networks and radial basis function neural networks.
    Type of Medium: Online Resource
    Pages: 1 online resource (88 pages)
    Edition: 1st ed.
    ISBN: 9783642025327
    Series Statement: Natural Computing Series
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=511564
    DDC: 006.32
    Language: English
    Note: Intro -- Preface -- Contents -- 1 Introduction to Neural Networks -- 1.1 Properties of Neural Networks -- 1.2 Neural Network Learning -- 1.2.1 Supervised Learning -- 1.2.2 Unsupervised Learning -- 1.3 Perceptron -- 1.4 Adaline and Least Mean Square Algorithm -- 1.5 Multilayer Perceptron and Backpropagation Algorithm -- 1.5.1 Output Layer Learning -- 1.5.2 Hidden Layer Learning -- 1.6 Radial Basis Function Networks -- 1.7 Support Vector Machines -- 2 Principles of Sensitivity Analysis -- 2.1 Perturbations in Neural Networks -- 2.2 Neural Network Sensitivity Analysis -- 2.3 Fundamental Methods of Sensitivity Analysis -- 2.3.1 Geometrical Approach -- 2.3.2 Statistical Approach -- 2.4 Summary -- 3 Hyper-Rectangle Model -- 3.1 Hyper-Rectangle Model for Input Space of MLP -- 3.2 Sensitivity Measure of MLP -- 3.3 Discussion -- 4 Sensitivity Analysis with Parameterized Activation Function -- 4.1 Parameterized Antisymmetric Squashing Function -- 4.2 Sensitivity Measure -- 4.3 Summary -- 5 Localized Generalization Error Model -- 5.1 Introduction -- 5.2 The Localized Generalization Error Model -- 5.2.1 The Q-Neighborhood and Q-Union -- 5.2.2 The Localized Generalization Error Bound -- 5.2.3 Stochastic Sensitivity Measure for RBFNN -- 5.2.4 Characteristics of the Error Bound -- 5.2.5 Comparing Two Classifiers Using the Error Bound -- 5.3 Architecture Selection Using the Error Bound -- 5.3.1 Parameters for MC2SG -- 5.3.2 RBFNN Architecture Selection Algorithm for MC2SG -- 5.3.3 A Heuristic Method to Reduce the Computational Time for MC2SG -- 5.4 Summary -- 6 Critical Vector Learning for RBF Networks -- 6.1 Related Work -- 6.2 Construction of RBF Networks with Sensitivity Analysis -- 6.2.1 RBF Classifiers' Sensitivity to the Kernel Function Centers -- 6.2.2 Orthogonal Least Square Transform -- 6.2.3 Critical Vector Selection -- 6.3 Summary. , 7 Sensitivity Analysis of Prior Knowledge -- 7.1 KBANNs -- 7.2 Inductive Bias -- 7.3 Sensitivity Analysis and Measures -- 7.3.1 Output-Pattern Sensitivity -- 7.3.2 Output-Weight Sensitivity -- 7.3.3 Output-H Sensitivity -- 7.3.4 Euclidean Distance -- 7.4 Promoter Recognition -- 7.4.1 Data and Initial Domain Theory -- 7.4.2 Experimental Methodology -- 7.5 Discussion and Conclusion -- 8 Applications -- 8.1 Input Dimension Reduction -- 8.1.1 Sensitivity Matrix -- 8.1.2 Criteria for Pruning Inputs -- 8.2 Network Optimization -- 8.3 Selective Learning -- 8.4 Hardware Robustness -- 8.5 Measure of Nonlinearity -- 8.6 Parameter Tuning for Neocognitron -- 8.6.1 Receptive Field -- 8.6.2 Selectivity -- 8.6.3 Sensitivity Analysis of the Neocognitron -- Bibliography.
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  • 4
    Electronic Resource
    Electronic Resource
    Handwritten Chinese character recognition by rule-embedded Neocognitron (1994)
    Springer
    Neural computing & applications 2 (1994), S. 216-226 
    Details
    ISSN: 1433-3058
    Keywords: Handwritten Chinese character recognition ; Structural approach ; Neural network ; Fuzzy attributed production rule ; Rule-embedded neocognitron ; Structure deviation tolerance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper presents an attempt to integrate neural computation with a domain knowledge technique to resolve the problem of the wide variety in handwritten Chinese characters. Despite their complexity, Chinese characters can be seen as structured patterns. Therefore, we propose a symbolic representation to describe these structural formations. In particular, we consider the Fuzzy Attributed Production Rule (FAPR) as a possible symbolic representation. On the neural computational side, we study Fukushima's Neocognitron model, which has been successfully demonstrated to recognize handwritten alphanumerics. Despite its power and tolerance capabilities, the supervised training scheme used by Fukushima is impractical for a large character set such as Chinese characters. We thus propose a ruleembedded Neocognitron network which can be readily mapped with structure-knowledge of Chinese characters as represented in FAPRs. In this paper, we demonstrate how 50 Chinese characters are mapped onto the network, and that the system performance in tolerating character structure deviations is satisfactory.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01414810
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    Paper (German National Licenses)
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