Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Computational intelligence comprises concepts, paradigms, algorithms, and implementations of systems that are intended to exhibit intelligent behavior in complex environments. It relies heavily on (at least) nature-inspired methods, which have the advantage that they tolerate incomplete, imprecise and uncertain knowledge and thus also facilitate finding solutions that are approximative, manageable and robust at the same time. Fully updated, this new edition of the authoritative textbook provides a clear and logical introduction to Computational Intelligence, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. Rather than aim for completeness, the goal is to give a methodical introduction, supporting fundamental concepts and their implementations with explanation of the theoretical background of proposed problem solutions. Topics and features: Offers new material on deep learning, scalarization, large-scale optimization algorithms, and collective decision-making algorithms Contains numerous classroom-tested examples and definitions Discusses in detail the classical areas of artificial neural networks, fuzzy systems, evolutionary algorithms, and Bayes and Markov networks Reviews the latest developments, including such topics as ant colony optimization and probabilistic graphical models Provides supplementary material, including module descriptions, lecture slides, exercises with solutions, and software tools This seminal textbook is primarily meant as a companion book for lectures on the covered topics in the area of computational intelligence. However, it is also eminently suitable as a guidebook for self-study by students and practitioners from industry and commerce. Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.

Artificial intelligence. --- Engineering mathematics. --- Engineering --- Computational intelligence. --- Computer science. --- Artificial Intelligence. --- Mathematical and Computational Engineering Applications. --- Computational Intelligence. --- Theory and Algorithms for Application Domains. --- Data processing. --- Intel·ligència computacional

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.

Machine learning. --- Artificial intelligence. --- Computer science. --- Machine Learning. --- Artificial Intelligence. --- Theory and Algorithms for Application Domains. --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Learning, Machine --- Artificial intelligence --- Metaheuristics. --- Heuristic algorithms

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is a hands-on guide for programmers who want to learn how C++ is used to develop solutions for options and derivatives trading in the financial industry. It explores the main algorithms and programming techniques used in implementing systems and solutions for trading options and derivatives. This updated edition will bring forward new advances in C++ software language and libraries, with a particular focus on the new C++23 standard. The book starts by covering C++ language features that are frequently used to write financial software for options and derivatives. These features include the STL (standard template library), generic templates, functional programming, and support for numerical code. Examples include additional support for lambda functions with simplified syntax, improvements in automatic type detection for templates, custom literals, modules, constant expressions, and improved initialization strategies for C++ objects. This book also provides how-to examples that cover all the major tools and concepts used to build working solutions for quantitative finance. It discusses how to create bug-free and efficient applications, leveraging the knowledge of object-oriented and template-based programming. It has two new chapters covering backtesting option strategies and processing financial data.. It introduces the topics covered in the book in a logical and structured way, with lots of examples that will bring them to life. Options and Derivatives Programming in C++23 has been written with the goal of reaching readers who are looking for a concise, algorithms-based book that provides basic information through well-targeted examples and ready to use solutions. You will: Gain insight into the fundamental challenges of the options and derivatives market Master the features of the C++ language used in quantitative financial programming Understand quantitative finance algorithms for options and derivatives Build pricing algorithms around the Black-Scholes model, and use binomial and differential equations methods.

Programming languages (Electronic computers). --- Compilers (Computer programs). --- Algorithms. --- Computer science. --- Business enterprises --- Programming Language. --- Compilers and Interpreters. --- Design and Analysis of Algorithms. --- Theory and Algorithms for Application Domains. --- Corporate Finance. --- Finance. --- C++ (Computer program language) --- Computer software. --- Programming languages (Electronic computers)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Numerical Algorithmic Science and Engineering (NAS&E), or more compactly, Numerical Algorithmics, is the theoretical and empirical study and the practical implementation and application of algorithms for solving finite-dimensional problems of a numeric nature. The variables of such problems are either discrete-valued, or continuous over the reals, or, and as is often the case, a combination of the two, and they may or may not have an underlying network/graph structure. This re-emerging discipline of numerical algorithmics within computer science is the counterpart of the now well-established discipline of numerical analysis within mathematics, where the latter’s emphasis is on infinite-dimensional, continuous numerical problems and their finite-dimensional, continuous approximates. A discussion of the underlying rationale for numerical algorithmics, its foundational models of computation, its organizational details, and its role, in conjunction with numerical analysis, in support of the modern modus operandi of scientific computing, or computational science & engineering, is the primary focus of this short monograph. It comprises six chapters, each with its own bibliography. Chapters 2, 3 and 6 present the book’s primary content. Chapters 1, 4, and 5 are briefer, and they provide contextual material for the three primary chapters and smooth the transition between them. Mathematical formalism has been kept to a minimum, and, whenever possible, visual and verbal forms of presentation are employed and the discussion enlivened through the use of motivating quotations and illustrative examples. The reader is expected to have a working knowledge of the basics of computer science, an exposure to basic linear algebra and calculus (and perhaps some real analysis), and an understanding of elementary mathematical concepts such as convexity of sets and functions, networks and graphs, and so on. Although this book is not suitable for use as the principal textbook for a course on numerical algorithmics (NAS&E), it will be of value as a supplementary reference for a variety of courses. It can also serve as the primary text for a research seminar. And it can be recommended for self-study of the foundations and organization of NAS&E to graduate and advanced undergraduate students with sufficient mathematical maturity and a background in computing. When departments of computer science were first created within universities worldwide during the middle of the twentieth century, numerical analysis was an important part of the curriculum. Its role within the discipline of computer science has greatly diminished over time, if not vanished altogether, and specialists in that area are now to be found mainly within other fields, in particular, mathematics and the physical sciences. A central concern of this monograph is the regrettable, downward trajectory of numerical analysis within computer science and how it can be arrested and suitably reconstituted. Resorting to a biblical metaphor, numerical algorithmics (NAS&E) as envisioned herein is neither old wine in new bottles, nor new wine in old bottles, but rather this re-emerging discipline is a decantation of an age-old vintage that can hopefully find its proper place within the larger arena of computer science, and at what appears now to be an opportune time.

Algorithms. --- Numerical analysis. --- Computer science. --- Mathematics—Data processing. --- Computer science—Mathematics. --- Discrete mathematics. --- Operations research. --- Design and Analysis of Algorithms. --- Numerical Analysis. --- Theory and Algorithms for Application Domains. --- Computational Mathematics and Numerical Analysis. --- Discrete Mathematics in Computer Science. --- Operations Research and Decision Theory. --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Informatics --- Science --- Mathematical analysis --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Algorism --- Algebra --- Arithmetic --- Foundations

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This textbook offers advanced content on computer vision (basic content can be found in its prerequisite textbook, “2D Computer Vision: Principles, Algorithms and Applications”), including the basic principles, typical methods and practical techniques. It is intended for graduate courses on related topics, e.g. Computer Vision, 3-D Computer Vision, Graphics, Artificial Intelligence, etc. The book is mainly based on my lecture notes for several undergraduate and graduate classes I have offered over the past several years, while a number of topics stem from my research publications co-authored with my students. This book takes into account the needs of learners with various professional backgrounds, as well as those of self-learners. Furthermore, it can be used as a reference guide for practitioners and professionals in related fields. To aid in comprehension, the book includes a wealth of self-test questions (with hints and answers). On the one hand, these questions help teachers to carry out online teaching and interact with students during lectures; on the other, self-learners can use them to assess whether they have grasped the key content.

Computer vision. --- Image processing—Digital techniques. --- Image processing. --- Computer science. --- User interfaces (Computer systems). --- Human-computer interaction. --- Computer Vision. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Image Processing. --- Theory and Algorithms for Application Domains. --- Computer Science. --- User Interfaces and Human Computer Interaction. --- Computer-human interaction --- Human factors in computing systems --- Interaction, Human-computer --- Human engineering --- User-centered system design --- User interfaces (Computer systems) --- Interfaces, User (Computer systems) --- Human-machine systems --- Human-computer interaction --- Informatics --- Science --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Visió per ordinador --- Processament digital d'imatges