Sampling Techniques For Supervised Or Unsupervised Tasks

Author : Frédéric Ros,Serge Guillaume
Publisher : Springer Nature
Page : 232 pages
File Size : 42,5 Mb
Release : 2019-10-26
Category : Technology & Engineering
ISBN : 9783030293499

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Sampling Techniques for Supervised or Unsupervised Tasks by Frédéric Ros,Serge Guillaume Pdf

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli

Author : Usman Qamar,Muhammad Summair Raza
Publisher : Springer Nature
Page : 492 pages
File Size : 54,6 Mb
Release : 2023-04-02
Category : Computers
ISBN : 9783031174421

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Data Science Concepts and Techniques with Applications by Usman Qamar,Muhammad Summair Raza Pdf

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Author : Luis Carlos Méndez-González,Luis Alberto Rodríguez-Picón,Iván Juan Carlos Pérez Olguín
Publisher : Springer Nature
Page : 338 pages
File Size : 54,8 Mb
Release : 2023-06-16
Category : Technology & Engineering
ISBN : 9783031297755

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Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems by Luis Carlos Méndez-González,Luis Alberto Rodríguez-Picón,Iván Juan Carlos Pérez Olguín Pdf

This book presents a series of applications of different techniques found in Industry 4.0 with relation to productivity, continuous improvement, quality, decision systems, software development, and automation systems. The techniques used throughout this book allow the reader to replicate the results obtained towards different types of companies that wish to undertake in the new era of the digital industrial revolution. This book can also help students from different areas of engineering to understand how the use of new technologies is applied to solve current relevant problems and how they give the possibility of constant innovation in the different industrial sectors. This is accomplished through the analysis of illustrative case studies, descriptive methodologies and structured insights that are provided through the different considered techniques.

Author : Mehdi Toloo,Siamak Talatahari,Iman Rahimi
Publisher : Academic Press
Page : 316 pages
File Size : 52,5 Mb
Release : 2022-02-09
Category : Science
ISBN : 9780128238004

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Multi-Objective Combinatorial Optimization Problems and Solution Methods by Mehdi Toloo,Siamak Talatahari,Iman Rahimi Pdf

Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considered metaheuristic, mathematical programming, heuristic, hyper heuristic and hybrid approaches. In other words, the book presents various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice. Combinatorial optimization problems appear in a wide range of applications in operations research, engineering, biological sciences and computer science, hence many optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic and algebraic techniques. This book covers this important topic as computational optimization has become increasingly popular as design optimization and its applications in engineering and industry have become ever more important due to more stringent design requirements in modern engineering practice. Presents a collection of the most up-to-date research, providing a complete overview of multi-objective combinatorial optimization problems and applications Introduces new approaches to handle different engineering and science problems, providing the field with a collection of related research not already covered in the primary literature Demonstrates the efficiency and power of the various algorithms, problems and solutions, including numerous examples that illustrate concepts and algorithms

Author : V. Bindhu,João Manuel R. S. Tavares,Alexandros-Apostolos A. Boulogeorgos,Chandrasekar Vuppalapati
Publisher : Springer Nature
Page : 821 pages
File Size : 47,5 Mb
Release : 2021-03-25
Category : Technology & Engineering
ISBN : 9789813349094

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International Conference on Communication, Computing and Electronics Systems by V. Bindhu,João Manuel R. S. Tavares,Alexandros-Apostolos A. Boulogeorgos,Chandrasekar Vuppalapati Pdf

This book includes high-quality papers presented at the International Conference on Communication, Computing and Electronics Systems 2020, held at the PPG Institute of Technology, Coimbatore, India, on 21–22 October 2020. The book covers topics such as automation, VLSI, embedded systems, integrated device technology, satellite communication, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, Internet of Things, precision models, bioinformatics, and healthcare informatics.

Author : Claudio Stamile,Aldo Marzullo,Enrico Deusebio
Publisher : Packt Publishing Ltd
Page : 338 pages
File Size : 48,7 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781800206755

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Graph Machine Learning by Claudio Stamile,Aldo Marzullo,Enrico Deusebio Pdf

Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Author : Ana I. Pereira,Andrej Košir,Florbela P. Fernandes,Maria F. Pacheco,João P. Teixeira,Rui P. Lopes
Publisher : Springer Nature
Page : 840 pages
File Size : 49,8 Mb
Release : 2023-01-01
Category : Computers
ISBN : 9783031232367

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Optimization, Learning Algorithms and Applications by Ana I. Pereira,Andrej Košir,Florbela P. Fernandes,Maria F. Pacheco,João P. Teixeira,Rui P. Lopes Pdf

This book constitutes the proceedings of the Second International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022, held in Bragança, Portugal, in October 2022. The 53 full papers and 3 short papers were thoroughly reviewed and selected from 145 submissions. They are organized in the topical sections on Machine and Deep Learning; Optimization; Artificial Intelligence; Optimization in Control Systems Design; Measurements with the Internet of Things; Trends in Engineering Education; Advances and Optimization in Cyber-Physical Systems; and Computer vision based on learning algorithms.

Author : Stavros Skouras,David M. A. Mehler,Amelie Haugg
Publisher : Frontiers Media SA
Page : 195 pages
File Size : 51,6 Mb
Release : 2024-04-04
Category : Science
ISBN : 9782832547335

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Translational Applications of Neuroimaging by Stavros Skouras,David M. A. Mehler,Amelie Haugg Pdf

Despite substantial progress in the development of neuroimaging methodologies, translational applications of neuroimaging remain scarce. This Research Topic invites article submissions that present promising neuroimaging applications and methods addressing critical needs for improving health outcomes. These may include Original Research, Clinical Trial, Systematic Review or Methods articles that investigate neuroimaging metrics as outcome measures or in combination with neural perturbation techniques (e.g., neurofeedback, neurostimulation), other translational applications (e.g., guiding neurosurgery). To foster debate, we also welcome critical Review, Opinion, and Perspective articles that survey the field and its progress towards clinical utility.

Author : Charu C. Aggarwal
Publisher : CRC Press
Page : 704 pages
File Size : 40,5 Mb
Release : 2014-07-25
Category : Business & Economics
ISBN : 9781466586758

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Data Classification by Charu C. Aggarwal Pdf

Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

Author : Ning Xiong,Maozhen Li,Kenli Li,Zheng Xiao,Longlong Liao,Lipo Wang
Publisher : Springer Nature
Page : 1527 pages
File Size : 55,6 Mb
Release : 2023-01-29
Category : Technology & Engineering
ISBN : 9783031207389

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery by Ning Xiong,Maozhen Li,Kenli Li,Zheng Xiao,Longlong Liao,Lipo Wang Pdf

This book consists of papers on the recent progresses in the state of the art in natural computation, fuzzy systems, and knowledge discovery. The book is useful for researchers, including professors, graduate students, as well as R & D staff in the industry, with a general interest in natural computation, fuzzy systems, and knowledge discovery. The work printed in this book was presented at the 2022 18th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery (ICNC-FSKD 2022), held from 30 July to 1 August 2022, in Fuzhou, China. All papers were rigorously peer-reviewed by experts in the areas.

Author : Stefan Michaelis,Nico Piatkowski,Marco Stolpe
Publisher : Springer
Page : 387 pages
File Size : 52,6 Mb
Release : 2016-07-02
Category : Computers
ISBN : 9783319417066

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Solving Large Scale Learning Tasks. Challenges and Algorithms by Stefan Michaelis,Nico Piatkowski,Marco Stolpe Pdf

In celebration of Prof. Morik's 60th birthday, this Festschrift covers research areas that Prof. Morik worked in and presents various researchers with whom she collaborated. The 23 refereed articles in this Festschrift volume provide challenges and solutions from theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms, feature selection and feature generation, optimization as well as efficiency of energy and communication.

Author : Osvaldo Gervasi,Beniamino Murgante,Sanjay Misra,Chiara Garau,Ivan Blečić,David Taniar,Bernady O. Apduhan,Ana Maria A. C. Rocha,Eufemia Tarantino,Carmelo Maria Torre
Publisher : Springer Nature
Page : 762 pages
File Size : 42,9 Mb
Release : 2021-09-11
Category : Computers
ISBN : 9783030869762

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Computational Science and Its Applications – ICCSA 2021 by Osvaldo Gervasi,Beniamino Murgante,Sanjay Misra,Chiara Garau,Ivan Blečić,David Taniar,Bernady O. Apduhan,Ana Maria A. C. Rocha,Eufemia Tarantino,Carmelo Maria Torre Pdf

The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, computational astrochemistry, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, computational geometry, applied mathematics human computer interaction, software design engineering, and others. Part V of the set includes the the proceedings on the following workshops: International Workshop on Computational Geometry and Applications (CGA 2021); International Workshop on Collaborative Intelligence in Multimodal Applications (CIMA 2021); International Workshop on Computational Science and HPC (CSHPC 2021); International Workshop on Computational Optimization and Applications (COA 2021); International Workshop on Cities, Technologies and Planning (CTP 2021); International Workshop on Computational Astrochemistry (CompAstro 2021); International Workshop on Advanced Modeling E-Mobility in Urban Spaces (DEMOS 2021).The chapters "On Local Convergence of Stochastic Global Optimization Algorithms" and "Computing Binding Energies of Interstellar Molecules by Semiempirical Quantum Methods: Comparison between DFT and GFN2 on Crystalline Ice" are published open access under a CC BY license (Creative Commons Attribution 4.0 International License).

Author : Zhidong Deng
Publisher : Springer Nature
Page : 735 pages
File Size : 41,9 Mb
Release : 2021-10-08
Category : Technology & Engineering
ISBN : 9789811663727

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Proceedings of 2021 Chinese Intelligent Automation Conference by Zhidong Deng Pdf

The proceedings present selected research papers from the CIAC2021, held in Zhanjiang, China on Nov 5-7, 2021. It covers a wide range of topics including intelligent control, robotics, artificial intelligence, pattern recognition, unmanned systems, IoT and machine learning. It includes original research and the latest advances in the field of intelligent automation. Engineers and researchers from academia, industry, and government can gain valuable insights into solutions combining ideas from multiple disciplines in this field.

Author : Lan Zou
Publisher : Elsevier
Page : 404 pages
File Size : 55,9 Mb
Release : 2022-11-05
Category : Computers
ISBN : 9780323903707

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Meta-Learning by Lan Zou Pdf

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. A comprehensive overview of state-of-the-art meta-learning techniques and methods associated with deep neural networks together with a broad range of application areas Coverage of nearly 200 state-of-the-art meta-learning algorithms, which are promoted by premier global AI conferences and journals, and 300 to 450 pieces of key research Systematic and detailed exploration of the most crucial state-of-the-art meta-learning algorithm mechanisms: model-based, metric-based, and optimization-based Provides solutions to the limitations of using deep learning and/or machine learning methods, particularly with small sample sizes and unlabeled data Gives an understanding of how meta-learning acts as a stepping stone to Artificial General Intelligence in 39 categories of tasks from 11 real-world application fields

Author : Amelia Manuti,Alessandro Lo Presti,Beatrice Van Der Heijden,Peter Kruyen,Ans De Vos,Monica Zaharie
Publisher : Frontiers Media SA
Page : 159 pages
File Size : 48,9 Mb
Release : 2022-07-13
Category : Science
ISBN : 9782889765614

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How Normal is the New Normal? Individual and Organizational Implications of the Covid 19 Pandemic by Amelia Manuti,Alessandro Lo Presti,Beatrice Van Der Heijden,Peter Kruyen,Ans De Vos,Monica Zaharie Pdf