Cover for Calculus of Thought

Calculus of Thought

Neuromorphic Logistic Regression in Cognitive Machines

Book2014

Author:

Daniel M. Rice

Calculus of Thought

Neuromorphic Logistic Regression in Cognitive Machines

Book2014

 

Cover for Calculus of Thought

Author:

Daniel M. Rice

About the book

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Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data n ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Calculus Ratiocinator

    Pages 1-25

  3. Book chapterAbstract only

    Chapter 2 - Most Likely Inference

    Pages 27-58

  4. Book chapterAbstract only

    Chapter 3 - Probability Learning and Memory

    Pages 59-93

  5. Book chapterAbstract only

    Chapter 4 - Causal Reasoning

    Pages 95-123

  6. Book chapterAbstract only

    Chapter 5 - Neural Calculus

    Pages 125-144

  7. Book chapterAbstract only

    Chapter 6 - Oscillating Neural Synchrony

    Pages 145-174

  8. Book chapterAbstract only

    Chapter 7 - Alzheimer's and Mind–Brain Problems

    Pages 175-195

  9. Book chapterAbstract only

    Chapter 8 - Let Us Calculate

    Pages 197-209

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    Appendix

    Pages 211-242

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    Notes and References

    Pages 243-270

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    Index

    Pages 271-280

About the book

Description

Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems.

The reduced error logistic regression (RELR) method is proposed as such a "Calculus of Thought." This book reviews how RELR's completely automated processing may parallel important aspects of explicit and implicit learning in neural processes. It emphasizes the fact that RELR is really just a simple adjustment to already widely used logistic regression, along with RELR's new applications that go well beyond standard logistic regression in prediction and explanation. Readers will learn how RELR solves some of the most basic problems in today’s big and small data related to high dimensionality, multi-colinearity, and cognitive bias in capricious outcomes commonly involving human behavior.

Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems.

The reduced error logistic regression (RELR) method is proposed as such a "Calculus of Thought." This book reviews how RELR's completely automated processing may parallel important aspects of explicit and implicit learning in neural processes. It emphasizes the fact that RELR is really just a simple adjustment to already widely used logistic regression, along with RELR's new applications that go well beyond standard logistic regression in prediction and explanation. Readers will learn how RELR solves some of the most basic problems in today’s big and small data related to high dimensionality, multi-colinearity, and cognitive bias in capricious outcomes commonly involving human behavior.

Key Features

  • Provides a high-level introduction and detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for a new era of smarter machines
  • Argues that smarter machine learning to handle both explanation and prediction without cognitive bias must have a foundation in cognitive neuroscience and must embody similar explicit and implicit learning principles that occur in the brain
  • Provides a high-level introduction and detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for a new era of smarter machines
  • Argues that smarter machine learning to handle both explanation and prediction without cognitive bias must have a foundation in cognitive neuroscience and must embody similar explicit and implicit learning principles that occur in the brain

Details

ISBN

978-0-12-410407-5

Language

English

Published

2014

Copyright

Copyright © 2014 Elsevier Inc. All rights reserved.

Imprint

Academic Press

Authors

Daniel M. Rice