Structure Learning with Constructive Neural Networks PDF Download
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Author: Jani Lahnajärvi
Publisher:
ISBN: 9789521507441
Category :
Languages : en
Pages : 114
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Book Description
Author: Jani Lahnajärvi
Publisher:
ISBN: 9789521507441
Category :
Languages : en
Pages : 114
Get Book Here
Book Description
Author: Tin-yau Kwok
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 328
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Book Description
Author: Leonardo Franco
Publisher: Springer Science & Business Media
ISBN: 3642045111
Category : Mathematics
Languages : en
Pages : 296
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Book Description
This book presents a collection of invited works that consider constructive methods for neural networks, taken primarily from papers presented at a special th session held during the 18 International Conference on Artificial Neural Networks (ICANN 2008) in September 2008 in Prague, Czech Republic. The book is devoted to constructive neural networks and other incremental learning algorithms that constitute an alternative to the standard method of finding a correct neural architecture by trial-and-error. These algorithms provide an incremental way of building neural networks with reduced topologies for classification problems. Furthermore, these techniques produce not only the multilayer topologies but the value of the connecting synaptic weights that are determined automatically by the constructing algorithm, avoiding the risk of becoming trapped in local minima as might occur when using gradient descent algorithms such as the popular back-propagation. In most cases the convergence of the constructing algorithms is guaranteed by the method used. Constructive methods for building neural networks can potentially create more compact and robust models which are easily implemented in hardware and used for embedded systems. Thus a growing amount of current research in neural networks is oriented towards this important topic. The purpose of this book is to gather together some of the leading investigators and research groups in this growing area, and to provide an overview of the most recent advances in the techniques being developed for constructive neural networks and their applications.
Author: Gen Matsumoto
Publisher: World Scientific
ISBN: 9814544353
Category :
Languages : en
Pages : 1119
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Book Description
Soft computing is the common name for a certain form of natural information processing that has its original form in biology, especially in the function of human brain. It is a discipline rooted in a group of technologies such as fuzzy logic, neural networks, chaos, genetic algorithms, probabilistic reasoning and learning algorithms. Today, soft computing has become an acknowledged concept; however, for a long time, such components of soft computing have been debated and individually developed.Since its beginning in 1990, the series of IIZUKA conferences has covered various kinds of technologies that constitute soft computing. This series has played a pioneering role in promoting the development of a symbiotic relationship between the various technologies of soft computing.At IIZUKA'98, the 5th International Conference on Soft Computing and Information/Intelligent Systems, new developments and results in this field were introduced and discussed by researchers from academic, governmental and industrial institutions around the world.This volume presents the opening lecture by Prof. Walter J Freeman, the keynote speech by Dr Gen Matsumoto, the plenary lectures by 5 eminent researchers and about 230 carefully selected papers drawn from more than 25 countries. It documents current research and in-depth studies on the fundamental aspects of soft computing and their practical applications.
Author: International Neural Network Society
Publisher: Psychology Press
ISBN: 9780805826081
Category : Neural networks (Computer science)
Languages : en
Pages : 1408
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Book Description
Author: Joan Cabestany
Publisher: Springer Science & Business Media
ISBN: 3540262083
Category : Computers
Languages : en
Pages : 1282
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Book Description
We present in this volume the collection of finally accepted papers of the eighth edition of the “IWANN” conference (“International Work-Conference on Artificial Neural Networks”). This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evolutionary systems). Since the first edition of IWANN in Granada (LNCS 540, 1991), the Artificial Neural Network (ANN) community, and the domain itself, have matured and evolved. Under the ANN banner we find a very heterogeneous scenario with a main interest and objective: to better understand nature and beings for the correct elaboration of theories, models and new algorithms. For scientists, engineers and professionals working in the area, this is a very good way to get solid and competitive applications. We are facing a real revolution with the emergence of embedded intelligence in many artificial systems (systems covering diverse fields: industry, domotics, leisure, healthcare, ... ). So we are convinced that an enormous amount of work must be, and should be, still done. Many pieces of the puzzle must be built and placed into their proper positions, offering us new and solid theories and models (necessary tools) for the application and praxis of these current paradigms. The above-mentioned concepts were the main reason for the subtitle of the IWANN 2005 edition: “Computational Intelligence and Bioinspired Systems.” The call for papers was launched several months ago, addressing the following topics: 1. Mathematical and theoretical methods in computational intelligence.
Author: Nate F. Kohl
Publisher:
ISBN:
Category :
Languages : en
Pages : 328
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Book Description
Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems -- such as those involving strategic decision-making -- have remained difficult to solve. This dissertation proposes the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. To evaluate this hypothesis, a method for measuring fracture using the concept of function variation of optimal policies is proposed. This metric is used to evaluate a popular neuroevolution algorithm, NEAT, empirically on a set of fractured problems. The results show that (1) NEAT does not usually perform well on such problems, and (2) the reason is that NEAT does not usually generate local decision regions, which would be useful in constructing a fractured decision boundary. To address this issue, two neuroevolution algorithms that model local decision regions are proposed: RBF-NEAT, which biases structural search by adding basis-function nodes, and Cascade-NEAT, which constrains structural search by constructing cascaded topologies. These algorithms are compared to NEAT on a set of fractured problems, demonstrating that this approach can improve performance significantly. A meta-level algorithm, SNAP-NEAT, is then developed to combine the strengths of NEAT, RBF-NEAT, and Cascade-NEAT. An evaluation in a set of benchmark problems shows that it is possible to achieve good performance even when it is not known a priori whether a problem is fractured or not. A final empirical comparison of these methods demonstrates that they can scale up to real-world tasks like keepaway and half-field soccer. These results shed new light on why constructive neuroevolution algorithms have difficulty in certain domains and illustrate how bias and constraint can be used to improve performance. Thus, this dissertation shows how neuroevolution can be scaled up from learning low-level control to learning strategic decision-making problems.
Author: Ke-Lin Du
Publisher: Springer Science & Business Media
ISBN: 1846283035
Category : Technology & Engineering
Languages : en
Pages : 610
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Book Description
This concise but comprehensive textbook reviews the most popular neural-network methods and their associated techniques. Each chapter provides state-of-the-art descriptions of important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms – powerful tools for neural-network learning – are introduced. The systematic survey of neural-network models and exhaustive references list will point readers toward topics for future research. The algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
Author: Maria Carmo Nicoletti
Publisher: Springer
ISBN: 3642018882
Category : Technology & Engineering
Languages : en
Pages : 349
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Book Description
Computational Intelligence (CI) and Bioprocess are well-established research areas which have much to offer each other. Under the perspective of the CI area, Biop- cess can be considered a vast application area with a growing number of complex and challenging tasks to be dealt with, whose solutions can contribute to boosting the development of new intelligent techniques as well as to help the refinement and s- cialization of many of the already existing techniques. Under the perspective of the Bioprocess area, CI can be considered a useful repertoire of theories, methods and techniques that can contribute and offer interesting alternative approaches for solving many of its problems, particularly those hard to solve using conventional techniques. Although throughout the past years CI and Bioprocess areas have accumulated substantial specific knowledge and progress has been quick and with a high degree of success, we believe there is still a long way to go in order to use the potentialities of the available CI techniques and knowledge at their full extent, as tools for supporting problem solving in bioprocesses. One of the reasons is the fact that both areas have progressed steadily and have been continuously accumulating and refining specific knowledge; another reason is the high level of technical expertise demanded by each of them. The acquisition of technical skills, experience and good insights in either of the two areas is very demanding and a hard task to be accomplished by any professional.
Author: Riichiro Mizoguchi
Publisher: Springer
ISBN: 3540445331
Category : Computers
Languages : en
Pages : 858
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Book Description
PRICAI 2000, held in Melbourne, Australia, is the sixth Pacific Rim Interna tional Conference on Artificial Intelligence and is the successor to the five earlier PRICAIs held in Nagoya (Japan), Seoul (Korea), Beijing (China), Cairns (Aus tralia) and Singapore in the years 1990, 1992, 1994, 1996 and 1998 respectively. PRICAI is the leading conference in the Pacific Rim region for the presenta tion of research in Artificial Intelligence, including its applications to problems of social and economic importance. The objectives of PRICAI are: To provide a forum for the introduction and discussion of new research results, concepts and technologies; To provide practising engineers with exposure to and an evaluation of evolving research, tools and practices; To provide the research community with exposure to the problems of practical applications of AI; and To encourage the exchange of AI technologies and experience within the Pacific Rim countries. PRICAI 2000 is a memorial event in the sense that it is the last one in the 20"" century. It reflects what researchers in this region believe to be promising for their future AI research activities. In fact, some salient features can be seen in the papers accepted. We have 12 papers on agents, while PRICAI 96 and 98 had no more than two or three. This suggests to us one of the directions in which AI research is going in the next century. It is true that agent research provides us with a wide range of research subjects from basic ones to applications.
