Nature-Inspired Computation in Data Mining and Machine Learning

Nature-Inspired Computation in Data Mining and Machine Learning
Author :
Publisher : Springer Nature
Total Pages : 282
Release :
ISBN-10 : 9783030285531
ISBN-13 : 3030285537
Rating : 4/5 (31 Downloads)

Synopsis Nature-Inspired Computation in Data Mining and Machine Learning by : Xin-She Yang

This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.

Nature-Inspired Algorithms and Applications

Nature-Inspired Algorithms and Applications
Author :
Publisher : John Wiley & Sons
Total Pages : 384
Release :
ISBN-10 : 9781119681663
ISBN-13 : 1119681669
Rating : 4/5 (63 Downloads)

Synopsis Nature-Inspired Algorithms and Applications by : S. Balamurugan

Mit diesem Buch soll aufgezeigt werden, wie von der Natur inspirierte Berechnungen eine praktische Anwendung im maschinellen Lernen finden, damit wir ein besseres Verständnis für die Welt um uns herum entwickeln. Der Schwerpunkt liegt auf der Darstellung und Präsentation aktueller Entwicklungen in den Bereichen, in denen von der Natur inspirierte Algorithmen speziell konzipiert und angewandt werden, um komplexe reale Probleme in der Datenanalyse und Mustererkennung zu lösen, und zwar durch Anwendung fachspezifischer Lösungen. Mit einer detaillierten Beschreibung verschiedener, von der Natur inspirierter Algorithmen und ihrer multidisziplinären Anwendung (beispielsweise in Maschinenbau und Elektrotechnik, beim maschinellen Lernen, in der Bildverarbeitung, beim Data Mining und in Drahtlosnetzwerken) ist dieses Buch ein praktisches Nachschlagewerk.

Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications

Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Author :
Publisher : IGI Global
Total Pages : 1810
Release :
ISBN-10 : 9781522507895
ISBN-13 : 1522507892
Rating : 4/5 (95 Downloads)

Synopsis Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources

As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
Author :
Publisher : Academic Press
Total Pages : 188
Release :
ISBN-10 : 9780128172179
ISBN-13 : 0128172177
Rating : 4/5 (79 Downloads)

Synopsis Introduction to Algorithms for Data Mining and Machine Learning by : Xin-She Yang

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Nature-Inspired Computation and Machine Learning

Nature-Inspired Computation and Machine Learning
Author :
Publisher : Springer
Total Pages : 549
Release :
ISBN-10 : 9783319136509
ISBN-13 : 331913650X
Rating : 4/5 (09 Downloads)

Synopsis Nature-Inspired Computation and Machine Learning by : Alexander Gelbukh

The two-volume set LNAI 8856 and LNAI 8857 constitutes the proceedings of the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla, Mexico, in November 2014. The total of 87 papers plus 1 invited talk presented in these proceedings were carefully reviewed and selected from 348 submissions. The first volume deals with advances in human-inspired computing and its applications. It contains 44 papers structured into seven sections: natural language processing, natural language processing applications, opinion mining, sentiment analysis, and social network applications, computer vision, image processing, logic, reasoning, and multi-agent systems, and intelligent tutoring systems. The second volume deals with advances in nature-inspired computation and machine learning and contains also 44 papers structured into eight sections: genetic and evolutionary algorithms, neural networks, machine learning, machine learning applications to audio and text, data mining, fuzzy logic, robotics, planning, and scheduling, and biomedical applications.

Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms
Author :
Publisher : Elsevier
Total Pages : 277
Release :
ISBN-10 : 9780124167452
ISBN-13 : 0124167454
Rating : 4/5 (52 Downloads)

Synopsis Nature-Inspired Optimization Algorithms by : Xin-She Yang

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. - Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature - Provides a theoretical understanding as well as practical implementation hints - Provides a step-by-step introduction to each algorithm

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing
Author :
Publisher : Springer Nature
Total Pages : 228
Release :
ISBN-10 : 9789811566950
ISBN-13 : 981156695X
Rating : 4/5 (50 Downloads)

Synopsis Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing by : Simon James Fong

This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.

Mathematical Foundations of Nature-Inspired Algorithms

Mathematical Foundations of Nature-Inspired Algorithms
Author :
Publisher : Springer
Total Pages : 114
Release :
ISBN-10 : 9783030169367
ISBN-13 : 3030169367
Rating : 4/5 (67 Downloads)

Synopsis Mathematical Foundations of Nature-Inspired Algorithms by : Xin-She Yang

This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
Author :
Publisher : Academic Press
Total Pages : 188
Release :
ISBN-10 : 9780128172162
ISBN-13 : 0128172169
Rating : 4/5 (62 Downloads)

Synopsis Introduction to Algorithms for Data Mining and Machine Learning by : Xin-She Yang

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.