Neural Networks and Statistical Learning, Du Ke-Lin, Swamy M. N. S.

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
Рейтинг:
Цена: 76850.00 T
Наличие на складе: Заказано в издательстве.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh
Название: Introduction to statistical learning
ISBN: 1071614177 ISBN-13(EAN): 9781071614174
Издательство: Springer
Рейтинг:
Цена: 55890.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.

Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.

An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Автор: Hiesinger Peter Robin
Название: The Self-Assembling Brain: How Neural Networks Grow Smarter
ISBN: 0691181225 ISBN-13(EAN): 9780691181226
Издательство: Wiley
Рейтинг:
Цена: 36960.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

What neurobiology and artificial intelligence tell us about how the brain builds itself

How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?

As Peter Robin Hiesinger argues, "the information problem" underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development--and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of "grown" networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.

Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.

Автор: Nikola K. Kasabov
Название: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
ISBN: 3662577135 ISBN-13(EAN): 9783662577134
Издательство: Springer
Рейтинг:
Цена: 260870.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.

Автор: Goldberg Yoav
Название: Neural Network Methods in Natural Language Processing
ISBN: 1627052984 ISBN-13(EAN): 9781627052986
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 76690.00 T
Наличие на складе: Нет в наличии.
Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Автор: Aggarwal Charu C.
Название: Neural Networks and Deep Learning: A Textbook
ISBN: 3030068560 ISBN-13(EAN): 9783030068561
Издательство: Springer
Рейтинг:
Цена: 46570.00 T
Наличие на складе: Нет в наличии.
Описание: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Автор: Le Lu
Название: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
ISBN: 3030139689 ISBN-13(EAN): 9783030139681
Издательство: Springer
Рейтинг:
Цена: 149060.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.

Автор: Kalyagin V. A., Koldanov A. P., Koldanov P. A.
Название: Statistical Analysis of Graph Structures in Random Variable Networks
ISBN: 3030602923 ISBN-13(EAN): 9783030602925
Издательство: Springer
Цена: 46570.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

1. Introduction.- 2. Random variable networks. -3. Network Identification Structure Algorithms.- 4. Uncertainty of Network Structure Identification.- 5. Robustness of Network Structure Identification.- 6. Optimality of Network Structure Identification.- 7. Applications to Market Network Analysis.- 8. Conclusion.- 9. References.

Автор: Borkotokey Surajit, Kumar Rajnish, Mukherjee Diganta
Название: Game Theory and Networks: New Perspectives and Directions
ISBN: 9811647364 ISBN-13(EAN): 9789811647369
Издательство: Springer
Рейтинг:
Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Agnieszka Rusinowska: On Different Ranking Methods.- Manipushpak Mitra and Suresh Mutuswami: No-Envy Allocations for Queueing Problems with Multiple Identical Machines.- Amarjyoti Mahanta: On Imitation Learning.- Soumendu Sarkar: Assembly Problems.- Sudipta Sarangi: Social Preferences and the Provision of Public Goods.- Robert P. Gilles: Building Social Networks under Constraints.- Sinan Ertemal and Rajnish Kumar: Rationing Rules under Uncertain Claims: A Survey.- Sujata Gowala and Surajit Borkotokey: A Class of Egalitarian Shapley Values.- Parishmita Boruah: New Characterizations of the Discounted Shapley Values.- Maria Zdimalova: Analysis of Biological Data by Graph Theory Approach.- S. Gokulraj and A. Chandrashekaran: Linear Games and Complementarity Problems.- Anindya Chakravarty, Anirban Chakraborty and Suryansh Upamanyu: A Complex Network View of the Economy.- Souvik Roy, Soumyarup Sadhukhany, and Arunava Sen: Recent Results on Strategy-Proofness of Random Social Choice Functions.

Автор: Sarunas Raudys
Название: Statistical and Neural Classifiers
ISBN: 1447110714 ISBN-13(EAN): 9781447110712
Издательство: Springer
Рейтинг:
Цена: 130430.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used..

Автор: Schalkoff
Название: Pattern Recognition Statistical Structural And Neu Neural Approaches (WSE)
ISBN: 0471529745 ISBN-13(EAN): 9780471529743
Издательство: Wiley
Рейтинг:
Цена: 272390.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An introduction to the concepts, methods and applications of pattern recognition, which demonstrates the similarities and differences among various approaches. Each chapter provides the reader with examples and material for a more in-depth study of specific topics.

Автор: Ke-Lin Du; M. N. S. Swamy
Название: Neural Networks and Statistical Learning
ISBN: 1447170474 ISBN-13(EAN): 9781447170471
Издательство: Springer
Рейтинг:
Цена: 95770.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Inclusive coverage of all the essential neural network applications in a statistical learning framework makes this a baseline text for students and researchers, with 25 chapters on all the major approaches that include a wealth of examples and exercises.