Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python
Autor Basilio de Braganca Pereira, Calyampudi Radhakrishna Rao, Fabio Borges de Oliveiraen Limba Engleză Hardback – 2 sep 2020
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 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.
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Specificații
ISBN-13: 9781138364509
ISBN-10: 1138364509
Pagini: 248
Ilustrații: 47 Tables, black and white; 117 Illustrations, black and white
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.5 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
ISBN-10: 1138364509
Pagini: 248
Ilustrații: 47 Tables, black and white; 117 Illustrations, black and white
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.5 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
Cuprins
1. Introduction. 2. Fundamental Concepts of Neural Networks. 3. Some Common Neural Network Models. 4 Multivariate Statistics and Neural Networks. 5. Regression Neural Network Models. 6. Survival Analysis and other Models.
Notă biografică
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 (1970–1989 and 1994–1997), Research Professor at COPPE (1970–present), Titular Professor of Applied Statistics at COPPE (1989–1994, retired), Titular Professor of Biostatistics at the School of Medicine (1998–2015, 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 Cramér–Rao inequality, Rao–Blackwellization, Rao distance, Fisher–Rao 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.
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 Cramér–Rao inequality, Rao–Blackwellization, Rao distance, Fisher–Rao 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.
Recenzii
'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
- Oke Gerke, International Society for Clinical Biostatistics, 71, 2021
Descriere
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.