Learning as the Evolution of Representation: Technical Report 477, November 1898 (Classic Reprint) - Hardcover

Pasquale Caianiello

 
9780484499514: Learning as the Evolution of Representation: Technical Report 477, November 1898 (Classic Reprint)

Synopsis

How learning reshapes the way we represent knowledge and build smarter machines.

This book analyzes learning as a practical, data-driven process. It weaves ideas from information theory with how codes and classifications interact to produce stable, unique representations of text and problems. The goal is to understand how to make learning efficient and computer implementations reliable.

Readers will explore concepts like semantic alphabets, complete schemes, and hierarchical alphabets, and how these ideas support flexible, adaptive computation. The work also touches on neural networks, language processing, and the broader implications for programming languages and computer systems.

  • Core idea: learning as efficient data acquisition and representation.
  • How codes and classifications lead to unambiguous, complete interpretations.
  • A framework for semantic universes and hierarchies of alphabets.
  • Connections to neural models, language processing, and practical computing.

Ideal for readers of learning theory, information science, and computational linguistics who want a foundational, theory-driven view of how representations evolve.

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