Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines
暫譯: 思維微積分:認知機器中的神經形態邏輯回歸

Daniel M Rice

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商品描述

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.

  • 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

商品描述(中文翻譯)

《思維微積分:認知機器中的神經形態邏輯回歸》是所有科學家必讀的書籍,介紹了一種旨在模擬大數據神經處理的非常簡單的計算方法。本書受到戈特弗里德·萊布尼茲的微積分推理者(Calculus Ratiocinator)理念的啟發,該理念認為機器計算應該發展成為模擬人類認知過程,從而避免在實際和科學問題的分析解決方案中出現有問題的主觀偏見。

本書提出了減少誤差的邏輯回歸(Reduced Error Logistic Regression, RELR)方法,作為這種「思維微積分」。本書回顧了RELR的完全自動化處理如何與神經過程中的顯性和隱性學習的重要方面相平行。它強調RELR實際上只是對已廣泛使用的邏輯回歸進行的簡單調整,並且RELR的新應用遠超過標準邏輯回歸在預測和解釋方面的能力。讀者將學習到RELR如何解決當今大數據和小數據中與高維度、多重共線性以及人類行為中常見的變幻結果相關的認知偏見等一些最基本的問題。

- 提供高層次的介紹和神經、統計及機器學習知識基礎的詳細回顧,為更智能機器的新時代奠定基礎
- 主張更智能的機器學習必須在認知神經科學的基礎上,並體現大腦中發生的類似顯性和隱性學習原則,以處理解釋和預測而不帶有認知偏見