Towards Advanced Data Analysis by Combining Soft Computing and Statistics
Towards Advanced Data Analysis by Combining Soft Computing and Statistics

Author: Christian Borgelt

Publisher: Springer

Published: 2012-08-29

Total Pages: 378

ISBN-13: 3642302785

DOWNLOAD EBOOK

Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty. Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance. Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.

Building Bridges between Soft and Statistical Methodologies for Data Science
Building Bridges between Soft and Statistical Methodologies for Data Science

Author: Luis A. García-Escudero

Publisher: Springer Nature

Published: 2022-08-24

Total Pages: 421

ISBN-13: 3031155092

DOWNLOAD EBOOK

Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, computer scientists, engineers, econometricians, and psychologists are more and more interested in facing this challenge. As a consequence, there is a clear need to build bridges between all these communities for Data Science. This book contains more than fifty selected recent contributions aiming to establish the above referred bridges. These contributions address very different and relevant aspects such as imprecise probabilities, information theory, random sets and random fuzzy sets, belief functions, possibility theory, dependence modelling and copulas, clustering, depth concepts, dimensionality reduction of complex data and robustness.

Towards the Future of Fuzzy Logic
Towards the Future of Fuzzy Logic

Author: Rudolf Seising

Publisher: Springer

Published: 2015-05-26

Total Pages: 382

ISBN-13: 3319187503

DOWNLOAD EBOOK

This book provides readers with a snapshot of the state-of-the art in fuzzy logic. Throughout the chapters, key theories developed in the last fifty years as well as important applications to practical problems are presented and discussed from different perspectives, as the authors hail from different disciplines and therefore use fuzzy logic for different purposes. The book aims at showing how fuzzy logic has evolved since the first theory formulation by Lotfi A. Zadeh in his seminal paper on Fuzzy Sets in 1965. Fuzzy theories and implementation grew at an impressive speed and achieved significant results, especially on the applicative side. The study of fuzzy logic and its practice spread all over the world, from Europe to Asia, America and Oceania. The editors believe that, thanks to the drive of young researchers, fuzzy logic will be able to face the challenging goals posed by computing with words. New frontiers of knowledge are waiting to be explored. In order to motivate young people to engage in the future development of fuzzy logic, fuzzy methodologies, fuzzy applications, etc., the editors invited a team of internationally respected experts to write the present collection of papers, which shows the present and future potentials of fuzzy logic from different disciplinary perspectives and personal standpoints.

Computational Intelligence
Computational Intelligence

Author: Rudolf Kruse

Publisher: Springer

Published: 2016-09-16

Total Pages: 556

ISBN-13: 1447172965

DOWNLOAD EBOOK

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced second edition has been fully revised and expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

Research in Data Science
Research in Data Science

Author: Ellen Gasparovic

Publisher: Springer

Published: 2019-03-25

Total Pages: 302

ISBN-13: 3030115666

DOWNLOAD EBOOK

This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia.

Computer Science and Educational Informatization
Computer Science and Educational Informatization

Author: Jianhou Gan

Publisher: Springer Nature

Published: 2024-02-10

Total Pages: 469

ISBN-13: 9819994926

DOWNLOAD EBOOK

These two volumes constitute the revised selected papers of the 5th International Conference, CSEI 2023, held in Kunming, China, during August 11–13, 2023. The 76 full papers and the 21 short papers included in this volume were carefully reviewed and selected from 297 submissions. They focus on computer science, education informatization and engineering education, innovative application for the deeper integration of education practice and information technology, educational informatization and big data for education.

Clustering High--Dimensional Data
Clustering High--Dimensional Data

Author: Francesco Masulli

Publisher: Springer

Published: 2015-11-24

Total Pages: 157

ISBN-13: 366248577X

DOWNLOAD EBOOK

This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.

Combining Soft Computing and Statistical Methods in Data Analysis
Combining Soft Computing and Statistical Methods in Data Analysis

Author: Christian Borgelt

Publisher: Springer Science & Business Media

Published: 2010-10-12

Total Pages: 640

ISBN-13: 3642147461

DOWNLOAD EBOOK

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics.

Synergies of Soft Computing and Statistics for Intelligent Data Analysis
Synergies of Soft Computing and Statistics for Intelligent Data Analysis

Author: Rudolf Kruse

Publisher: Springer Science & Business Media

Published: 2012-09-07

Total Pages: 555

ISBN-13: 364233041X

DOWNLOAD EBOOK

In recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS). This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.

Machine Learning and Cybernetics
Machine Learning and Cybernetics

Author: Xizhao Wang

Publisher: Springer

Published: 2014-12-04

Total Pages: 460

ISBN-13: 3662456524

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.