Handbook of Machine Learning for Computational Optimization PDF Download
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Author: Vishal Jain
Publisher: CRC Press
ISBN: 100045567X
Category : Technology & Engineering
Languages : en
Pages : 295
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Book Description
Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies
Author: Vishal Jain
Publisher: CRC Press
ISBN: 100045567X
Category : Technology & Engineering
Languages : en
Pages : 295
Get Book
Book Description
Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies
Author: Suvrit Sra
Publisher: MIT Press
ISBN: 026201646X
Category : Computers
Languages : en
Pages : 509
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Book Description
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Author: Tshilidzi Marwala
Publisher: World Scientific
ISBN: 981120568X
Category : Computers
Languages : en
Pages : 321
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Book Description
Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.
Author: Pijush Samui
Publisher: Academic Press
ISBN: 0128113197
Category : Technology & Engineering
Languages : en
Pages : 658
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Book Description
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Author: Plamen Parvanov Angelov
Publisher: World Scientific
ISBN: 9811247331
Category : Computers
Languages : en
Pages : 1057
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Book Description
The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new edition of the earlier Handbook on Computational Intelligence.This two-volume handbook is divided into five parts. Volume 1 covers Explainable AI and Supervised Learning. Volume 2 covers three parts: Deep Learning, Intelligent Control, and Evolutionary Computation. The chapters detail the theory, methodology and applications of computer learning and intelligence, and are authored by some of the leading experts in the respective areas. The fifteen core chapters of the previous edition have been written and significantly refreshed by the same authors. Parts of the handbook have evolved to keep pace with the latest developments in computational intelligence in the areas that span across Machine Learning and Artificial Intelligence. The Handbook remains dedicated to applications and engineering-orientated aspects of these areas over abstract theories.Related Link(s)
Author: Hossain, Md Shamim
Publisher: IGI Global
ISBN: 1668471078
Category : Computers
Languages : en
Pages : 445
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Book Description
In the modern data-driven era, artificial intelligence (AI) and machine learning (ML) technologies that allow a computer to mimic intelligent human behavior are essential for organizations to achieve business excellence and assist organizations in extracting useful information from raw data. AI and ML have existed for decades, but in the age of big data, this sort of analysis is in higher demand than ever, especially for customer support and analytics. The Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics investigates the applications of AI and ML and how they can be implemented to enhance customer support and analytics at various levels of organizations. This book is ideal for marketing professionals, managers, business owners, researchers, practitioners, academicians, instructors, university libraries, and students, and covers topics such as artificial intelligence, machine learning, supervised learning, deep learning, customer sentiment analysis, data mining, neural networks, and business analytics.
Author: Rohit Anand
Publisher: CRC Press
ISBN: 1000906108
Category : Computers
Languages : en
Pages : 376
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Book Description
Integration of IoT with Cloud Computing for Smart Applications provides an integrative overview of the Internet of Things (IoT) and cloud computing to be used for the various futuristic and intelligent applications. The aim of this book is to integrate IoT and cloud computing to translate ordinary resources into smart things. Discussions in this book include a broad and integrated perspective on the collaboration, security, growth of cloud infrastructure, and real-time data monitoring. Features: Presents an integrated approach to solve the problems related to security, reliability, and energy consumption. Explains a unique approach to discuss the research challenges and opportunities in the field of IoT and cloud computing. Discusses a novel approach for smart agriculture, smart healthcare systems, smart cities and many other modern systems based on machine learning, artificial intelligence, and big data, etc. Information presented in a simplified way for students, researchers, academicians and scientists, business innovators and entrepreneurs, management professionals and practitioners. This book can be great reference for graduate and postgraduate students, researchers, and academicians working in the field of computer science, cloud computing, artificial intelligence, etc.
Author: Achim Zielesny
Publisher: Springer Science & Business Media
ISBN: 3642212808
Category : Technology & Engineering
Languages : en
Pages : 465
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Book Description
The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. These sections may be skipped without affecting the main road but they will open up possibly interesting insights beyond the mere data massage. All topics are completely demonstrated with the aid of the commercial computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source so the detailed code of every method is freely accessible. All examples and applications shown throughout the book may be used and customized by the reader without any restrictions. The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction to these topics. Readers with programming skills may easily port and customize the provided code.
Author: Janusz Kacprzyk
Publisher: Springer
ISBN: 3662435055
Category : Technology & Engineering
Languages : en
Pages : 1634
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Book Description
The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.
Author: Jin-Chuan Duan
Publisher: Springer Science & Business Media
ISBN: 9783642172540
Category : Business & Economics
Languages : en
Pages : 804
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Book Description
Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools.
