Principles of Quantum Artificial Intelligence

Book Description

In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main of quantum computation — Quantum Fourier transform and Grover search. Then, we indicate how these two can be applied to problem solving and finally present a general model of a quantum that is based on production systems.

Readership: Professionals, academics, researchers and graduate students in artificial , theoretical computer science, quantum and computational .

Table of Contents

Chapter 1. Introduction
Chapter 2. Two Basic Methods for Variable Assessment
Chapter 3. CHAID-Based Data Mining for Paired-Variable Assessment
Chapter 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice
Chapter 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data
Chapter 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment
Chapter 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
Chapter 8. Logistic Regression: The Workhorse of Response
Chapter 9. Ordinary Regression: The Workhorse of Profit Modeling

Book Details

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EU(multi) Click to downloadPDF408/17/2014
UpLoaded Click to downloadPDF409/16/2014
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