Machine Learning Paradigms Theory And Application

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Machine Learning Paradigms: Theory and Application

Aboul Ella Hassanien
Machine Learning Paradigms Theory and Application Book PDF

Author : Aboul Ella Hassanien
Publisher : Springer
Page : 474 pages
File Size : 51,7 Mb
Release : 2018-12-08
Category : Technology & Engineering
ISBN : 9783030023577

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Machine Learning Paradigms: Theory and Application by Aboul Ella Hassanien Pdf

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.

Fusion of Machine Learning Paradigms

Ioannis K. Hatzilygeroudis,George A. Tsihrintzis,Lakhmi C. Jain
Fusion of Machine Learning Paradigms Book PDF

Author : Ioannis K. Hatzilygeroudis,George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer Nature
Page : 204 pages
File Size : 41,5 Mb
Release : 2023-02-06
Category : Technology & Engineering
ISBN : 9783031223716

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Fusion of Machine Learning Paradigms by Ioannis K. Hatzilygeroudis,George A. Tsihrintzis,Lakhmi C. Jain Pdf

This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.

Machine Learning Paradigms

George A. Tsihrintzis,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer Nature
Page : 429 pages
File Size : 44,7 Mb
Release : 2020-07-23
Category : Computers
ISBN : 9783030497248

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Machine Learning Paradigms by George A. Tsihrintzis,Lakhmi C. Jain Pdf

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

Theory and Novel Applications of Machine Learning

Er Meng Joo,Yi Zhou
Theory and Novel Applications of Machine Learning Book PDF

Author : Er Meng Joo,Yi Zhou
Publisher : BoD – Books on Demand
Page : 390 pages
File Size : 55,5 Mb
Release : 2009-01-01
Category : Computers
ISBN : 9783902613554

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Theory and Novel Applications of Machine Learning by Er Meng Joo,Yi Zhou Pdf

Even since computers were invented, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science. Machine Learning (ML) draws upon ideas from a diverse set of disciplines, including AI, Probability and Statistics, Computational Complexity, Information Theory, Psychology and Neurobiology, Control Theory and Philosophy. ML involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc. Real-world applications of ML are widespread such as Pattern Recognition, Data Mining, Gaming, Bio-science, Telecommunications, Control and Robotics applications. This books reports the latest developments and futuristic trends in ML.

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David
Understanding Machine Learning Book PDF

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 41,5 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

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Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Machine Learning Paradigms

George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain
Publisher : Springer
Page : 370 pages
File Size : 54,8 Mb
Release : 2018-07-03
Category : Technology & Engineering
ISBN : 9783319940304

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Machine Learning Paradigms by George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain Pdf

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Computational Intelligence Paradigms

S. Sumathi,Surekha Paneerselvam
Computational Intelligence Paradigms Book PDF

Author : S. Sumathi,Surekha Paneerselvam
Publisher : CRC Press
Page : 853 pages
File Size : 45,8 Mb
Release : 2010-01-05
Category : Computers
ISBN : 9781439809037

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Computational Intelligence Paradigms by S. Sumathi,Surekha Paneerselvam Pdf

Offering a wide range of programming examples implemented in MATLAB, Computational Intelligence Paradigms: Theory and Applications Using MATLAB presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and pr

Machine Learning Paradigms

George A. Tsihrintzis,Maria Virvou,Evangelos Sakkopoulos,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Maria Virvou,Evangelos Sakkopoulos,Lakhmi C. Jain
Publisher : Springer
Page : 0 pages
File Size : 51,7 Mb
Release : 2019-07-15
Category : Technology & Engineering
ISBN : 3030156273

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Machine Learning Paradigms by George A. Tsihrintzis,Maria Virvou,Evangelos Sakkopoulos,Lakhmi C. Jain Pdf

This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.

Computational Intelligence Paradigms

S.. PANEERSELVAM SUMATHI (SUREKHA.),Surekha Paneerselvam
Computational Intelligence Paradigms Book PDF

Author : S.. PANEERSELVAM SUMATHI (SUREKHA.),Surekha Paneerselvam
Publisher : CRC Press
Page : 851 pages
File Size : 44,9 Mb
Release : 2019-08-30
Category : Electronic
ISBN : 0367384558

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Computational Intelligence Paradigms by S.. PANEERSELVAM SUMATHI (SUREKHA.),Surekha Paneerselvam Pdf

Offering a wide range of programming examples implemented in MATLAB(R), Computational Intelligence Paradigms: Theory and Applications Using MATLAB(R) presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi-Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization. Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

Emerging Paradigms in Machine Learning

Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett
Emerging Paradigms in Machine Learning Book PDF

Author : Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett
Publisher : Springer Science & Business Media
Page : 498 pages
File Size : 53,6 Mb
Release : 2012-07-31
Category : Technology & Engineering
ISBN : 9783642286995

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Emerging Paradigms in Machine Learning by Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett Pdf

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Aboul Ella Hassanien,Ashraf Darwish
Machine Learning and Big Data Analytics Paradigms Analysis Applications and Challenges Book PDF

Author : Aboul Ella Hassanien,Ashraf Darwish
Publisher : Springer Nature
Page : 648 pages
File Size : 54,6 Mb
Release : 2020-12-14
Category : Computers
ISBN : 9783030593384

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges by Aboul Ella Hassanien,Ashraf Darwish Pdf

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Algorithms in Machine Learning Paradigms

Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Paramartha Dutta,Kousik Dasgupta
Algorithms in Machine Learning Paradigms Book PDF

Author : Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Paramartha Dutta,Kousik Dasgupta
Publisher : Springer Nature
Page : 201 pages
File Size : 44,7 Mb
Release : 2020-01-03
Category : Technology & Engineering
ISBN : 9789811510410

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Algorithms in Machine Learning Paradigms by Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Paramartha Dutta,Kousik Dasgupta Pdf

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

Machine Learning

Seyedeh Leili Mirtaheri,Reza Shahbazian
Machine Learning Book PDF

Author : Seyedeh Leili Mirtaheri,Reza Shahbazian
Publisher : CRC Press
Page : 212 pages
File Size : 49,7 Mb
Release : 2022-09-29
Category : Business & Economics
ISBN : 9781000737691

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Machine Learning by Seyedeh Leili Mirtaheri,Reza Shahbazian Pdf

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary, this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms, covers state-of-the-art, practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications.

Machine Learning Paradigms

George A. Tsihrintzis,Dionisios N. Sotiropoulos,L. C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Dionisios N. Sotiropoulos,L. C. Jain
Publisher : Unknown
Page : 128 pages
File Size : 55,8 Mb
Release : 2019
Category : Data mining
ISBN : 3319940317

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Machine Learning Paradigms by George A. Tsihrintzis,Dionisios N. Sotiropoulos,L. C. Jain Pdf

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Machine Learning: From Theory to Applications

Stephen J. Hanson,Werner Remmele,Ronald L. Rivest
Machine Learning From Theory to Applications Book PDF

Author : Stephen J. Hanson,Werner Remmele,Ronald L. Rivest
Publisher : Springer Science & Business Media
Page : 292 pages
File Size : 40,9 Mb
Release : 1993-03-30
Category : Computers
ISBN : 3540564837

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Machine Learning: From Theory to Applications by Stephen J. Hanson,Werner Remmele,Ronald L. Rivest Pdf

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.