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Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python [Kõva köide]

, , (University of Buffalo - SUNY)
  • Formaat: Hardback, 234 pages, kõrgus x laius: 234x156 mm, kaal: 498 g, 47 Tables, black and white; 117 Illustrations, black and white
  • Ilmumisaeg: 02-Sep-2020
  • Kirjastus: CRC Press
  • ISBN-10: 1138364509
  • ISBN-13: 9781138364509
Teised raamatud teemal:
Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with PythonSuurem pilt
  • Formaat: Hardback, 234 pages, kõrgus x laius: 234x156 mm, kaal: 498 g, 47 Tables, black and white; 117 Illustrations, black and white
  • Ilmumisaeg: 02-Sep-2020
  • Kirjastus: CRC Press
  • ISBN-10: 1138364509
  • ISBN-13: 9781138364509
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138364509.html
Märksõnad:
"This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples. Statistical topics covered include multivariate statistics (Cluster,Classification, Dimension Reduction, Projection Pursuit, Nonlinear Regression) Survival Analysis (Cox Model and Extensions) Control, Chart and Statistical Inference. Illustrative examples will be mainly from medicine, engineering, and economics"--

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.

Key Features:

  • Discusses applications in several research areas

  • Covers a wide range of widely used statistical methodologies

  • Includes Python code examples

  • Gives numerous neural network models

This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.

This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Arvustused

'Statistical Learning Using Neural Networks is a user-friendly introductory textbook into a timely topic of increasing presence in the daily work of biostatisticians involved in collaborative research with clinicians.'

- Oke Gerke, International Society for Clinical Biostatistics, 71, 2021

Preface xi
Acknowledgments xiii
1 Introduction
1(2)
2 Fundamental Concepts on Neural Networks
3(30)
2.1 Artificial Intelligence: Symbolist and Connectionist
3(1)
2.2 The Brain and Neural Networks
4(1)
2.3 Artificial Neural Networks and Diagrams
5(3)
2.4 Activation Functions
8(1)
2.5 Network Architectures
8(4)
2.6 Network Training
12(1)
2.7 Kolmogorov Theorem
13(2)
2.8 Model Choice
15(4)
2.8.1 Generalization
16(1)
2.8.2 Bias-variance Trade-off: Early Stopping Method of Training
16(1)
2.8.3 Choice of Structure
17(1)
2.8.4 Network Growing
18(1)
2.8.5 Network Pruning
18(1)
2.9 McCulloch-Pitt Neuron
19(1)
2.10 Rosenblatt Perceptron
20(2)
2.11 Widrow's Adaline and Madaline
22(1)
2.12 Terminology
23(5)
2.13 Running Python in a Nutshell
28(5)
3 Some Common Neural Network Models
33(34)
3.1 Multilayer Feedforward Networks
33(4)
3.2 Associative and Hopfield Networks
37(8)
3.3 Radial Basis Function Networks
45(1)
3.4 Wavelet Neural Networks
46(5)
3.4.1 Wavelets
48(3)
3.4.2 Wavelet Networks and Radial Basis Wavelet Networks
51(1)
3.5 Mixture-of-Experts Networks
51(4)
3.6 Neural Network and Statistical Model Interfaces
55(1)
3.7 Some Common Neural Networks in Python
56(11)
3.7.1 Fitting Data
56(2)
3.7.2 Classification
58(4)
3.7.3 Hopfield Networks
62(5)
4 Multivariate Statistics Neural Network Models
67(64)
4.1 Cluster and Scaling Networks
67(20)
4.1.1 Competitive Networks
67(3)
4.1.2 Learning Vector Quantization (LVQ)
70(2)
4.1.3 Adaptive Resonance Theory (ART) Networks
72(6)
4.1.4 Self-Organizing Maps (SOM) Networks
78(9)
4.2 Dimensional Reduction Networks
87(21)
4.2.1 Basic Structure of Data Matrix
88(2)
4.2.2 Mechanics of Some Dimensional Reduction Techniques
90(1)
4.2.2.1 Principal Components Analysis (PCA)
90(1)
4.2.2.2 Nonlinear Principal Components
91(1)
4.2.2.3 Factor Analysis (FA)
91(1)
4.2.2.4 Correspondence Analysis (CA)
91(1)
4.2.2.5 Multidimensional Scaling
92(1)
4.2.2.6 Independent Component Analysis (ICA)
93(4)
4.2.3 PCA Networks
97(5)
4.2.4 Nonlinear PCA Networks
102(1)
4.2.5 FA Networks
102(1)
4.2.6 Correspondence Analysis (CA) Networks
103(3)
4.2.7 Independent Component Analysis (ICA) Networks
106(2)
4.3 Classification Networks
108(12)
4.4 Multivariate Statistics Neural Network Models with Python
120(11)
4.4.1 Clustering
121(5)
4.4.2 Fitting Data
126(5)
5 Regression Neural Network Models
131(20)
5.1 Generalized Linear Model Networks (GLIMNs)
131(8)
5.1.1 Logistic Regression Networks
132(4)
5.1.2 Regression Networks
136(3)
5.2 Nonparametric Regression and Classification Networks
139(8)
5.2.1 Probabilistic Neural Networks (PNNs)
139(1)
5.2.2 General Regression Neural Networks (GRNNs)
140(1)
5.2.3 Generalized Additive Model Networks
141(2)
5.2.4 Regression and Classification Tree Networks
143(2)
5.2.5 Projection Pursuit and Feedforward Networks
145(1)
5.2.6 Example
146(1)
5.3 Regression Neural Network Models with Python
147(4)
6 Survival Analysis and Other Networks
151(32)
6.1 Survival Analysis Networks
151(7)
6.2 Time Series Forecasting
158(9)
6.2.1 Forecasting with Neural Networks
163(4)
6.3 Control Chart Networks
167(3)
6.4 Some Statistical Inference Results
170(7)
6.4.1 Estimation Methods
171(1)
6.4.2 Bayesian Methods
172(3)
6.4.3 Interval Estimation
175(1)
6.4.4 Statistical Tests
176(1)
6.5 Forecasting with Python
177(6)
A Command Reference 183(32)
Bibliography 215(18)
Index 233
Basilio de Bragança Pereira, DIC and PhD (Imperial Collage), is Professor Emeritus of the Federal University of Rio de Janeiro (UFRJ) where he has worked since 1970, in the Institute of Mathematics, Postgraduate School of Engineering (COPPE) and School of Medicine. Associate Professor at the Institute of Mathematics (19701989 and 19941997), Research Professor at COPPE (1970present), Titular Professor of Applied Statistics at COPPE (19891994, retired), Titular Professor of Biostatistics at the School of Medicine (19982015, retired). Since 2018, he is a courtesy researcher at National Laboratory for Scientific Computing (LNCC).

Calyampudi Radhakrishna Rao, PhD and DSc (Cambridge), is Fellow of Royal Society known as C R Rao. He is Professor Emeritus at Pennsylvania State University and Research Professor at the University at Buffalo. Rao was awarded the US National Medal of Science in 2002 and the Guy Medal of the Royal Statistical Society in 1965, Silver, and in 2011, Gold. He is one of the top 10 Indian scientists of all time. He received 38 honorary doctoral degrees from universities in 19 countries. He is well-known for CramérRao inequality, RaoBlackwellization, Rao distance, FisherRao metric, among other important concepts introduced by him.

Fábio Borges de Oliveira, Dr.-Ing. (TU Darmstadt), is Professor at National Laboratory for Scientific Computing (LNCC) where he gives lectures on cryptography and on artificial intelligence applied to security and privacy for PhD students. He also works in the areas of smart grids, high performance computing, and algorithms. From 1994 to 2002, he worked at Londrina State University, where he provided support to its Computational Mathematics Lab. He was lecturer and taught several subjects. He is an IEEE Senior Member and received the Latin America Distinguished Service Award by IEEE Communications Society in 2018.