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|a MAIN
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|a Kyan, Matthew.
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|a Unsupervised learning :
|b a dynamic approach /
|c Matthew Kyan, Paisarn Muneesawang, Kambiz Jarrah, Ling Guan.
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|a Hoboken, New Jersey :
|b John Wiley & Sons, Inc.,
|c [2014]
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a IEEE Press series on computational intelligence
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|a Print version record and CIP data provided by publisher.
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| 504 |
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|a Includes bibliographical references and index.
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|a IEEE Press; Titlepage; Copyright; Acknowledgments; 1 Introduction; 1.1 Part I: The Self-Organizing Method; 1.2 Part II: Dynamic Self-Organization for Image Filtering and Multimedia Retrieval; 1.3 Part III: Dynamic Self-Organization for Image Segmentation and Visualization; 1.4 Future Directions; 2 Unsupervised Learning; 2.1 Introduction; 2.2 Unsupervised Clustering; 2.3 Distance Metrics for Unsupervised Clustering; 2.4 Unsupervised Learning Approaches; 2.5 Assessing Cluster Quality and Validity; 3 Self-Organization; 3.1 Introduction; 3.2 Principles of Self-Organization
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|a 3.3 Fundamental Architectures3.4 Other Fixed Architectures for Self-Organization; 3.5 Emerging Architectures for Self-Organization; 3.6 Conclusion; 4 Self-Organizing Tree Map; 4.1 Introduction; 4.2 Architecture; 4.3 Competitive Learning; 4.4 Algorithm; 4.5 Evolution; 4.6 Practical Considerations, Extensions, and Refinements; 4.7 Conclusions; Notes; 5 Self-Organization in Impulse Noise Removal; 5.1 Introduction; 5.2 Review of Traditional Median-Type Filters; 5.3 The Noise-Exclusive Adaptive Filtering; 5.4 Experimental Results; 5.5 Detection-Guided Restoration and Real-Time Processing
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8 |
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|a 5.6 Conclusions6 Self-Organization in Image Retrieval; 6.1 Retrieval of Visual Information; 6.2 Visual Feature Descriptor; 6.3 User-Assisted Retrieval; 6.4 Self-Organization for Pseudo Relevance Feedback; 6.5 Directed Self-Organization; 6.6 Optimizing Self-Organization for Retrieval; 6.7 Retrieval Performance; 6.8 Summary; 7 The Self-Organizing Hierarchical Variance Map:; 7.1 An Intuitive Basis; 7.2 Model Formulation and Breakdown; 7.3 Algorithm; 7.4 Simulations and Evaluation; 7.5 Tests on Self-Determination and the Optional Tuning Stage
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|a 7.6 Cluster Validity Analysis on Synthetic and UCI Data7.7 Summary; 8 Microbiological Image Analysis Using Self-Organization; 8.1 Image Analysis in the Biosciences; 8.2 Image Analysis Tasks Considered; 8.3 Microbiological Image Segmentation; 8.4 Image Segmentation Using Hierarchical Self-Organization; 8.5 Harvesting Topologies to Facilitate Visualization; 8.6 Summary; Notes; 9 Closing Remarks and Future Directions; 9.1 Summary of Main Findings; 9.2 Future Directions; Appendix A; A.1 Global and Local Consistency Error; References; Index; IEEE Press Series; End User License Agreement
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|a Database management.
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|a Self-organizing systems.
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|a Machine learning.
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|a Big data.
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|a COMPUTERS
|x Databases
|x General.
|2 bisacsh
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|a COMPUTERS
|x Desktop Applications
|x Databases.
|2 bisacsh
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|a COMPUTERS
|x System Administration
|x Storage & Retrieval.
|2 bisacsh
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|a Big data.
|2 fast
|0 (OCoLC)fst01892965
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| 650 |
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|a Database management.
|2 fast
|0 (OCoLC)fst00888037
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| 650 |
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|a Machine learning.
|2 fast
|0 (OCoLC)fst01004795
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|a Self-organizing systems.
|2 fast
|0 (OCoLC)fst01111791
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|a Electronic books.
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|a Electronic books.
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| 700 |
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|a Muneesawang, Paisarn.
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|a Jarrah, Kambiz.
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| 700 |
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|a Guan, Ling.
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|i Print version:
|a Kyan, Matthew.
|t Unsupervised learning.
|d Hoboken, New Jersey : John Wiley & Sons Inc., [2014]
|z 9780470278338
|w (DLC) 2013046024
|
| 830 |
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0 |
|a IEEE series on computational intelligence.
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| 856 |
4 |
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|u https://doi.org/10.1002/9781118875568
|z Full Text via HEAL-Link
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|a 92
|b DG1
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