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General principal analysis.pdf,非常不错的PCA分析的书,值得下载

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Air and Spaceborne Radar Systems: An Introduction

From the Publisher A practical tool on radar systems that will be of major help to technicians, student engineers and engineers working in industry and in radar research and development. The many users of radar as well as systems engineers and designers will also find it highly useful. Also of inter

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Curvature in mathematics and physics

This original text for courses in differential geometry is geared toward advanced undergraduate and graduate majors in math and physics. Based on an advanced class taught by a world-renowned mathematician for more than fifty years, the treatment introduces semi-Riemannian geometry and its principal

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Pattern Recognition with Neural Networks in C++

Preface Acknowledgment Chapter 1—Introduction 1.1 Pattern Recognition Systems 1.2 Motivation For Artificial Neural Network Approach 1.3 A Prelude To Pattern Recognition 1.4 Statistical Pattern Recognition 1.5 Syntactic Pattern Recognition

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Modern Antennas,2nd-2005

Contents List of contributors xvi Foreword xvii Acknowledgements xx Electromagnetism and antennas – a historical perspective 1 1 Fundamentals of electromagnetism 7 1.1 Maxwell’s equations 7 1.1.1 Maxwell’s equations in an arbitrary medium 7 1.1.2 Linear media 10 1.1.3 Conducting media 12 1.1.4 Recip

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Scientific Computing with Python 3

Scientific Computing with Python 3 English | 23 Dec. 2016 | ISBN: 1786463512 | 332 Pages | AZW3/MOBI/EPUB/PDF (conv) | 17.95 MB Key Features Your ultimate resource for getting up and running with Python numerical computations Explore numerical computing and mathematical libraries using Python 3.x c

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Scientific.Computing.with.Python.3.2nd.Ed.epub

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to

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Fuzzy and Neuro-Fuzzy Systems in Medicine

Preface About the Editors Part 1—Fundamentals and Neuro-Fuzzy Signal Processing Chapter 1—Fuzzy Logic and Neuro-Fuzzy Systems in Medicine and Bio-Medical Engineering: A Historical Perspective 1. The First Period: The Infancy 2. Further Developments and B

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PARAMETER ESTIMATION AND INVERSE PROBLEMS(2013)

This textbook evolved from a course in geophysical inverse methods taught during the past two decades at New Mexico Tech, first by Rick Aster and, subsequently, jointly between Rick Aster and Brian Borchers. The audience for the course has included a broad range of first- or second-year graduate stude

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Robust Statistics

1 Introduction 1 1.1 Classical and robust approaches to statistics 1 1.2 Mean and standard deviation 2 1.3 The “three-sigma edit” rule 5 1.4 Linear regression 7 1.4.1 Straight-line regression 7 1.4.2 Multiple linear regression 9 1.5 Correlation coefficients 11 1.6 Other parametric models 13 1.7 Prob

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TeeChart2013_130818_SourceCode

=============================================== TeeChart Pro v2013 Copyright (c) 1995-2013 by Steema Software All Rights Reserved =============================================== SOFTWARE LICENSING CONTRACT NOTICE TO USER: THIS IS A CONTRACT. BY CLICKING THE 'OK' BUTTON BELOW DURING INSTALLAT

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TeeChart2013_131216_SourceCode

=============================================== TeeChart Pro v2013 Copyright (c) 1995-2013 by Steema Software All Rights Reserved =============================================== SOFTWARE LICENSING CONTRACT NOTICE TO USER: THIS IS A CONTRACT. BY CLICKING THE 'OK' BUTTON BELOW DURING INSTALLAT

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Handbook of Research on Soft Computing and Nature-Inspired Algorithms

Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms i

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understanding machine learning theory-algorithms

1 Introduction 19 1.1 What Is Learning? 19 1.2 When Do We Need Machine Learning? 21 1.3 Types of Learning 22 1.4 Relations to Other Fields 24 1.5 How to Read This Book 25 1.5.1 Possible Course Plans Based on This Book 26 1.6 Notation 27 Part I Foundations 31 2 A Gentle Start 33 2.1 A Formal Model {

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计算机视觉(英文版)

CONTENTS I IMAGEFORMATION 1 1 RADIOMETRY — MEASURING LIGHT 3 1.1 Light in Space 3 1.1.1 Foreshortening 3 1.1.2 Solid Angle 4 1.1.3 Radiance 6 1.2 Light at Surfaces 8 1.2.1 Simplifying Assumptions 9 1.2.2 The Bidirectional Reflectance Distribution Function 9 1.3 Important Special Cases 11 1.3.1 Radio

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Course Overview ............................................................................ viii Course Goals................................................................................ viii Course Objectives .......................................................................... viii Unit 1

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Channel Coding in Communication Networks

Homage to Alain Glavieux. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Chapter 1. Information Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Gérard BATTAIL 1.1. Introduction: the Shannon paradigm . . . . . . . . . . . . . . . . . . . . . 1 1.2. Principal coding fun

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Ppattern Recognition and Machine Learning

Christopher M. Bishop 1 Introduction 1 1.1 Example: Polynomial Curve Fitting . . . . . . . . . . . . . . . . . 4 1.2 Probability Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2.1 Probability densities . . . . . . . . . . . . . . . . . . . . . 17 1.2.2 Expectations and covariances .

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Preface vii Mathematical notation xi Contents xiii 1 Introduction 1 1.1 Example: Polynomial Curve Fitting . . . . . . . . . . . . . . . . . 4 1.2 Probability Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2.1 Probability densities . . . . . . . . . . . . . . . . . . . . . 17 1.2.2 E

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注意:此书为英文版 Contents Preface vii Mathematical notation xi Contents xiii 1 Introduction 1 1.1 Example: Polynomial Curve Fitting . . . . . . . . . . . . . . . . . 4 1.2 Probability Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2.1 Probability densities . . . . . . . . . . . . . . . . .

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