Sampling Techniques For Supervised Or Unsupervised Tasks

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

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.

Second Edition offers a comprehensive presentation of scientific sampling principles and shows how to design a sample survey and analyze the resulting data. Demonstrates the validity of theorems and statements without resorting to detailed proofs.

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

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.

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).

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.

Over the last few decades, important progresses in the methods of sampling have been achieved. This book draws up an inventory of new methods that can be useful for selecting samples. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods. This book is aimed at experienced statisticians who are familiar with the theory of survey sampling.

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.

The quality of a piece of research not only stands or falls by the appropriateness of methodology and instrumentation but also by the suitability of the sampling strategy that has been adopted. Questions of sampling arise directly out of the issue of defining the population on which the research will focus. Researcher must take sampling decisions early in the overall planning of a piece of research. After taking decision about the selection and identification of the problem, the objectives and hypotheses of the research study, and the research design (experimental, survey, developmental, descriptive, case study, ethnography etc.), the researcher supposed to take decision about data gathering to realize the objectives of the research study.