VI Self-Organizing Maps and Learning Vector Quantization for Complex Data

Chapters in this book

1. Frontmatter 1
2. TABLE OF CONTENTS 4
3. ON THE BOOK SERIES 6
4. Introduction — Coding as Literacy 12
5. What Makes the SELF-ORGANIZING MAPS (SOM) so Particular among Learning Algorithms? 22
6. I Elements of a Digital Architecture 28
7. II A Nonanthropocentric Approach to Apperception 116
8. III Pre-Specific Modeling: Computational Machines in a Coexistence with Concrete Universals and Data Streams 132
9. IV SOM. Self. Organized 168
10. V The Nature of Local/ Global Distinctions, Group Actions, and Phases: A Sheaf-Theoretic Approach to Quantum Geometric Spectra 172
11. VI Self-Organizing Maps and Learning Vector Quantization for Complex Data 188
12. VII The Common Sense of Quantum Theory: Exploring the Internal Relational Structure of Self- Organization in Nature 214
13. VIII GICA: Grounded Intersubjective Concept Analysis. A Method for Improved Research, Communication, and Participation 236
14. IX “Ichnography”—The Nude and Its Model.The Alphabetic Absolute and Storytelling in the Grammatical Case of the Cryptographic Locative 276
15. Image references 350
16. Colophon 352