Calculus of Thought

Neuromorphic Logistic Regression in Cognitive Machines
Erscheinungsjahr 2014
ISBN: 978-0-12-410407-5
Verlag: Elsevier Inc

Buch, Englisch, 272 Seiten, Format (B × H): 156 mm x 236 mm, Gewicht: 519 g

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.

Zielgruppe

<p>Data Mining, Applied Math and Statistics, Modeling, and Cognitive Neuroscience</p>

Autoren/Hrsg.

Rice, Daniel M

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Preface: A Personal Perspective1. Calculus Ratiocinator2. Most Likely Inference3. Conditional Probability Learning4. Causal Reasoning5. Neural Calculus6. Oscillating Neural Synchrony7. Neural Natural Selection and Alzheimer’s Disease8. Let Us CalculateAppendix One: The RELR FormulationAppendix Two: The 2004 Election Weekend Survey Model

Über Autor(innen)

Rice, Daniel M
Daniel M. Rice is Principal and Senior Scientist of Rice Analytics. He founded the business in early 1996 as a sole proprietorship, but it was incorporated into its current structure in 2006. Prior to 1996, he was an assistant professor at the University of California-Irvine and the University of Southern California. Dan has almost 25 years of research project and advanced statistical modeling experience for major organizations that include the National Institute on Aging, Eli Lilly, Anheuser-Busch, Sears Portrait Studios, Hewlett-Packard, UBS, and Bank of America. He has a Ph.D. from the University of New Hampshire in Cognitive Neuroscience and Postdoctoral training in Applied Statistics from the University of California-Irvine. Dan is a previous recipient of an Individual National Research Service Award from the National Institutes of Health and is author of more than 20 publications, many of which are in conference proceedings and peer-reviewed journals in cognitive neuroscience and statistics.