neural networks and statistical learning

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Neural Networks And Statistical Learning

Author : Ke-Lin Du
ISBN : 9781447155713
Genre : Computers
File Size : 57. 98 MB
Format : PDF
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Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Statistical Learning Using Neural Networks

Author : Basilio de Bragança Pereira
ISBN : 1138364509
Genre : Computational learning theory
File Size : 28. 11 MB
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"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"--

Neural Networks

Author : Jong-Hoon Oh
ISBN : 9810223242
Genre : Science
File Size : 90. 98 MB
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The Elements Of Statistical Learning

Author : Trevor Hastie
ISBN : 9780387216065
Genre : Mathematics
File Size : 47. 2 MB
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Pattern Recognition And Neural Networks

Author : Brian D. Ripley
ISBN : 0521717701
Genre : Computers
File Size : 88. 3 MB
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Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

Learning From Data

Author : Vladimir Cherkassky
ISBN : 0470140518
Genre : Computers
File Size : 63. 65 MB
Format : PDF, Kindle
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An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Artificial Neural Networks And Statistical Pattern Recognition

Author : I.K. Sethi
ISBN : 9781483297873
Genre : Computers
File Size : 87. 93 MB
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With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

Machine Learning Mathematics

Author : Samuel Hack
ISBN : 1699742154
Genre :
File Size : 83. 2 MB
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Master the World of Machine Learning - Even if You're a Complete Beginner. Are you an aspiring entrepreneur? Or are you an amateur software developer looking for a break in the world of machine learning? Then this is the book for you. Machine learning is the way of the future - and breaking into this highly lucrative and ever-evolving field is a great way for your career, or business, to prosper. Inside this guide, you'll find simple, easy-to-follow explanations of the fundamental concepts behind machine learning, from the mathematical and statistical concepts to the programming behind them. With a wide range of comprehensive advice including machine learning models, neural networks, statistics, and much more, this guide is a highly effective tool for mastering this incredible technology. Inside, you will: Learn the Fundamental Concepts of Machine Learning Algorithms, and Their Impact in Resolving Modern Day Business Problems Understand The Four Fundamental Types of Machine Learning Algorithm Master the Concept of "Statistical Learning", a Descriptive Statistics-Based Machine Learning Algorithm Dive into the Development and Application of Six of the Most Popular Supervised and Unsupervised Machine Learning Algorithms, With Details on Linear Regression, Logistic Regression And More Learn Everything You Need to Know about Neural Networks and Data Pipelines Master the Concept of "General Setting of Learning", a Fundamental of Machine Learning Development Overview The Basics, Importance, and Applications of Data Science With Details on the "Team Data Science Process" Lifecycle And Much More! Covering everything you need to know about machine learning, now you can master the mathematics and statistics behind this field and develop your very own neural networks! Whether you want to use machine learning to help your business, or you're a programmer looking to expand your skills, this book is a must-read for anyone interested in the world of machine learning. Buy now to discover how you can master machine learning today! Scroll Up and Click the BUY NOW Button to Get Your Copy!

Artificial Neural Networks In Biomedicine

Author : Paulo J.G. Lisboa
ISBN : 1852330058
Genre : Computers
File Size : 51. 49 MB
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This volume provides a state-of-the-art survey of artificial neural network applications in biomedical diagnosis, laboratory data analysis and related practical areas. It looks at biomedical applications which involve customising neural network technology to resolve specific difficulties with data processing, and deals with applications relating to particular aspects of clinical practice and laboratory or medically-related analysis. Each chapter is self-contained with regard to the technology used, covering important technical points and implementation issues like the design of user interfaces and hardware/software platforms. Artificial Neural Networks in Biomedicine will be of interest to computer scientists and neural network practitioners who want to extend their knowledge of issues relevant to biomedical applications, developers of clinical computer systems, and medical researchers looking for new methods and computational tools.

Neural Networks For Intelligent Signal Processing

Author : Anthony Zaknich
ISBN : 9789812383051
Genre : Technology & Engineering
File Size : 25. 97 MB
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This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.

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