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Dynamic Fuzzy Logic and Its Applications
Dynamic fuzzy problem are problems that are universally focused by academies. Mathematicians and cybernetic experts have used fuzzy logic to developed theories and solve static problems in so called subjective and objective worlds. This book includes 12 chapters. Chapter 1 is about basic conceptions of Dynamic Fuzzy Sets (DFS). Chapter 2 introduces Dynamic Fuzzy (DF) decomposition theorem. Chapter 3 is about L form of DFS module structure. Chapter 4 is about representation theorem of DFS. Chapter 5 introduces extension theorem of DFS. Chapter 6 is about DF measure theory. In chapter 7 it is Dynamic Fuzzy Logic (DFL). Chapter 8 is about reasoning methods of DFL. Chapter 9 is about bases of DFL programming language. Chapter 10 introduces multi-agent learning model based on DFL. Chapter 11 is about autonomic computing model based on DFL. The last Chapter introduces application of DFL in machine learning.
Rough Sets
This book constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in November 2022. The 28 full papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Invited papers, IRSS President Forum; rough set theory and applications; granular computing and applications; classification and deep learning; conceptual knowledge discovery and machine learning based on three-way decisions and granular computing; uncertainty in three-way decisions; granular computing, and data science.
The Mathematics of Machine Learning
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
Artificial Intelligence
This book constitutes the refereed proceedings of the First CCF International Conference on Artificial Intelligence, CCF-ICAI 2018, held in Jinan, China in August, 2018. The 17 papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in topical sections on unsupervised learning, graph-based and semi-supervised learning, neural networks and deep learning, planning and optimization, AI applications.
Neural Information Processing
The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The 4th volume, LNCS 11304, is organized in topical sections on feature selection, clustering, classification, and detection.
Neural Information Processing
The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.
Rough Set and Knowledge Technology
This book constitutes the refereed proceedings of the 5th International Conference on Rough Set and Knowledge Technology, RSKT 2010, held in Beijing, China, in October 2010. The 98 revised full papers papers presented were carefully reviewed and selected from 175 initial submissions. The papers are organized in topical sections on rough sets and computing theory, fuzzy sets, knowledge technology, intelligent information processing, health informatics and biometrics authentication, neural networks, complex networks, granular computing, metaheuristic, cloud model and its application, data mining in cloud computing, decision-theoretic rough set model, and quotient space theory research and application.
Artificial Neural Networks and Machine Learning – ICANN 2023
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.
MultiMedia Modeling
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