Unsupervised learning : a dynamic approach /

• Κύριος συγγραφέας: Kyan, Matthew
• Άλλοι συγγραφείς: Muneesawang, Paisarn, Jarrah, Kambiz, Guan, Ling
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken, New Jersey : John Wiley & Sons, Inc., [2014]
• Σειρά: IEEE series on computational intelligence.
• Θέματα: Database management.; Self-organizing systems.; Machine learning.; Big data.; COMPUTERS > Databases > General.; COMPUTERS > Desktop Applications > Databases.; COMPUTERS > System Administration > Storage & Retrieval.; Electronic books.

Πίνακας περιεχομένων:

• 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
• 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
• 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
• 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