Unsupervised learning : (Record no. 59949)
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| fixed length control field | 09875nam a2201105 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6836130 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421114641.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151222s2014 nju ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781118875568 |
| -- | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | cloth |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | cloth |
| 100 1# - AUTHOR NAME | |
| Author | Kyan, Matthew, |
| 245 10 - TITLE STATEMENT | |
| Title | Unsupervised learning : |
| Sub Title | a dynamic approach / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (288 pages). |
| 490 1# - SERIES STATEMENT | |
| Series statement | IEEE Press series on computational intelligence |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Acknowledgments xi -- 1 Introduction 1 -- 1.1 Part I: The Self-Organizing Method 1 -- 1.2 Part II: Dynamic Self-Organization for Image Filtering and Multimedia Retrieval 2 -- 1.3 Part III: Dynamic Self-Organization for Image Segmentation and Visualization 5 -- 1.4 Future Directions 7 -- 2 Unsupervised Learning 9 -- 2.1 Introduction 9 -- 2.2 Unsupervised Clustering 9 -- 2.3 Distance Metrics for Unsupervised Clustering 11 -- 2.4 Unsupervised Learning Approaches 13 -- 2.4.1 Partitioning and Cluster Membership 13 -- 2.4.2 Iterative Mean-Squared Error Approaches 15 -- 2.4.3 Mixture Decomposition Approaches 17 -- 2.4.4 Agglomerative Hierarchical Approaches 18 -- 2.4.5 Graph-Theoretic Approaches 20 -- 2.4.6 Evolutionary Approaches 20 -- 2.4.7 Neural Network Approaches 21 -- 2.5 Assessing Cluster Quality and Validity 21 -- 2.5.1 Cost Function-Based Cluster Validity Indices 22 -- 2.5.2 Density-Based Cluster Validity Indices 23 -- 2.5.3 Geometric-Based Cluster Validity Indices 24 -- 3 Self-Organization 27 -- 3.1 Introduction 27 -- 3.2 Principles of Self-Organization 27 -- 3.2.1 Synaptic Self-Amplification and Competition 27 -- 3.2.2 Cooperation 28 -- 3.2.3 Knowledge Through Redundancy 29 -- 3.3 Fundamental Architectures 29 -- 3.3.1 Adaptive Resonance Theory 29 -- 3.3.2 Self-Organizing Map 37 -- 3.4 Other Fixed Architectures for Self-Organization 43 -- 3.4.1 Neural Gas 44 -- 3.4.2 Hierarchical Feature Map 45 -- 3.5 Emerging Architectures for Self-Organization 46 -- 3.5.1 Dynamic Hierarchical Architectures 47 -- 3.5.2 Nonstationary Architectures 48 -- 3.5.3 Hybrid Architectures 50 -- 3.6 Conclusion 50 -- 4 Self-Organizing Tree Map 53 -- 4.1 Introduction 53 -- 4.2 Architecture 54 -- 4.3 Competitive Learning 55 -- 4.4 Algorithm 57 -- 4.5 Evolution 61 -- 4.5.1 Dynamic Topology 61 -- 4.5.2 Classification Capability 64 -- 4.6 Practical Considerations, Extensions, and Refinements 68 -- 4.6.1 The Hierarchical Control Function 68 -- 4.6.2 Learning, Timing, and Convergence 71 -- 4.6.3 Feature Normalization 73. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 4.6.4 Stop Criteria 73 -- 4.7 Conclusions 74 -- 5 Self-Organization in Impulse Noise Removal 75 -- 5.1 Introduction 75 -- 5.2 Review of Traditional Median-Type Filters 76 -- 5.3 The Noise-Exclusive Adaptive Filtering 82 -- 5.3.1 Feature Selection and Impulse Detection 82 -- 5.3.2 Noise Removal Filters 84 -- 5.4 Experimental Results 86 -- 5.5 Detection-Guided Restoration and Real-Time Processing 99 -- 5.5.1 Introduction 99 -- 5.5.2 Iterative Filtering 101 -- 5.5.3 Recursive Filtering 104 -- 5.5.4 Real-Time Processing of Impulse Corrupted TV Pictures 105 -- 5.5.5 Analysis of the Processing Time 109 -- 5.6 Conclusions 115 -- 6 Self-Organization in Image Retrieval 119 -- 6.1 Retrieval of Visual Information 120 -- 6.2 Visual Feature Descriptor 122 -- 6.2.1 Color Histogram and Color Moment Descriptors 122 -- 6.2.2 Wavelet Moment and Gabor Texture Descriptors 123 -- 6.2.3 Fourier and Moment-based Shape Descriptors 125 -- 6.2.4 Feature Normalization and Selection 127 -- 6.3 User-Assisted Retrieval 130 -- 6.3.1 Radial Basis Function Method 132 -- 6.4 Self-Organization for Pseudo Relevance Feedback 136 -- 6.5 Directed Self-Organization 140 -- 6.5.1 Algorithm 142 -- 6.6 Optimizing Self-Organization for Retrieval 146 -- 6.6.1 Genetic Principles 147 -- 6.6.2 System Architecture 149 -- 6.6.3 Genetic Algorithm for Feature Weight Detection 150 -- 6.7 Retrieval Performance 153 -- 6.7.1 Directed Self-Organization 153 -- 6.7.2 Genetic Algorithm Weight Detection 155 -- 6.8 Summary 157 -- 7 The Self-Organizing Hierarchical Variance Map 159 -- 7.1 An Intuitive Basis 160 -- 7.2 Model Formulation and Breakdown 162 -- 7.2.1 Topology Extraction via Competitive Hebbian Learning 163 -- 7.2.2 Local Variance via Hebbian Maximal Eigenfilters 165 -- 7.2.3 Global and Local Variance Interplay for Map Growth and Termination 170 -- 7.3 Algorithm 173 -- 7.3.1 Initialization, Continuation, and Presentation 173 -- 7.3.2 Updating Network Parameters 175 -- 7.3.3 Vigilance Evaluation and Map Growth 175 -- 7.3.4 Topology Adaptation 176. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 7.3.5 Node Adaptation 177 -- 7.3.6 Optional Tuning Stage 177 -- 7.4 Simulations and Evaluation 177 -- 7.4.1 Observations of Evolution and Partitioning 178 -- 7.4.2 Visual Comparisons with Popular Mean-Squared Error Architectures 181 -- 7.4.3 Visual Comparison Against Growing Neural Gas 183 -- 7.4.4 Comparing Hierarchical with Tree-Based Methods 183 -- 7.5 Tests on Self-Determination and the Optional Tuning Stage 187 -- 7.6 Cluster Validity Analysis on Synthetic and UCI Data 187 -- 7.6.1 Performance vs. Popular Clustering Methods 190 -- 7.6.2 IRIS Dataset 192 -- 7.6.3 WINE Dataset 195 -- 7.7 Summary 195 -- 8 Microbiological Image Analysis Using Self-Organization 197 -- 8.1 Image Analysis in the Biosciences 197 -- 8.1.1 Segmentation: The Common Denominator 198 -- 8.1.2 Semi-supervised versus Unsupervised Analysis 199 -- 8.1.3 Confocal Microscopy and Its Modalities 200 -- 8.2 Image Analysis Tasks Considered 202 -- 8.2.1 Visualising Chromosomes During Mitosis 202 -- 8.2.2 Segmenting Heterogeneous Biofilms 204 -- 8.3 Microbiological Image Segmentation 205 -- 8.3.1 Effects of Feature Space Definition 207 -- 8.3.2 Fixed Weighting of Feature Space 209 -- 8.3.3 Dynamic Feature Fusion During Learning 213 -- 8.4 Image Segmentation Using Hierarchical Self-Organization 215 -- 8.4.1 Gray-Level Segmentation of Chromosomes 215 -- 8.4.2 Automated Multilevel Thresholding of Biofilm 220 -- 8.4.3 Multidimensional Feature Segmentation 221 -- 8.5 Harvesting Topologies to Facilitate Visualization 226 -- 8.5.1 Topology Aware Opacity and Gray-Level Assignment 227 -- 8.5.2 Visualization of Chromosomes During Mitosis 228 -- 8.6 Summary 233 -- 9 Closing Remarks and Future Directions 237 -- 9.1 Summary of Main Findings 237 -- 9.1.1 Dynamic Self-Organization: Effective Models for Efficient Feature Space Parsing 237 -- 9.1.2 Improved Stability, Integrity, and Efficiency 238 -- 9.1.3 Adaptive Topologies Promote Consistency and Uncover Relationships 239 -- 9.1.4 Online Selection of Class Number 239. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 9.1.5 Topologies Represent a Useful Backbone for Visualization or Analysis 240 -- 9.2 Future Directions 240 -- 9.2.1 Dynamic Navigation for Information Repositories 241 -- 9.2.2 Interactive Knowledge-Assisted Visualization 243 -- 9.2.3 Temporal Data Analysis Using Trajectories 245 -- Appendix A 249 -- A.1 Global and Local Consistency Error 249 -- References 251 -- Index 269. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Guan, Ling, |
| 700 1# - AUTHOR 2 | |
| Author 2 | Muneesawang, Paisarn, |
| 700 1# - AUTHOR 2 | |
| Author 2 | Jarrah, Kambiz, |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6836130 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Hoboken, New Jersey : |
| -- | John Wiley & Sons Inc., |
| -- | [2014] |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2014] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/22/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Database management. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Self-organizing systems. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data. |
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| -- | Aerospace electronics |
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| -- | Biological cells |
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| -- | Biology |
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| -- | Biomedical imaging |
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| -- | Data mining |
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| -- | Data models |
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| -- | Data visualization |
| 695 ## - | |
| -- | Equations |
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| -- | Euclidean distance |
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| -- | Feature extraction |
| 695 ## - | |
| -- | Filtering theory |
| 695 ## - | |
| -- | Gain control |
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| -- | Histograms |
| 695 ## - | |
| -- | Image analysis |
| 695 ## - | |
| -- | Image coding |
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| -- | Image color analysis |
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| -- | Image segmentation |
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| -- | Indexing |
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| -- | Information filters |
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| -- | Materials |
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| -- | Maximum likelihood detection |
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| -- | Microscopy |
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| -- | Multimedia communication |
| 695 ## - | |
| -- | Network topology |
| 695 ## - | |
| -- | Neurons |
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| -- | Noise |
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| -- | Nonlinear filters |
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| -- | Optical microscopy |
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| -- | Plastics |
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| -- | Probes |
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| -- | Prototypes |
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| -- | Sections |
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| -- | Subspace constraints |
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| -- | Support vector machine classification |
| 695 ## - | |
| -- | Topology |
| 695 ## - | |
| -- | Unsupervised learning |
| 695 ## - | |
| -- | Vectors |
| 695 ## - | |
| -- | Visualization |
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| -- | Wavelet transforms |
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