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Books in Neural networks

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11-20 of 38 results in All results

Artificial Neural Network for Drug Design, Delivery and Disposition

  • 1st Edition
  • October 15, 2015
  • Munish Puri + 4 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 1 5 5 9 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 1 7 4 4 - 9
Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs. This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences.

Artificial Neural Networks and Statistical Pattern Recognition

  • 1st Edition
  • Volume 11
  • June 28, 2014
  • I.K. Sethi + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 8 7 - 3
With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

Artificial Neural Networks

  • 1st Edition
  • June 28, 2014
  • K. Mäkisara + 3 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 8 0 0 - 9
This two-volume proceedings compiles a selection of research papers presented at the ICANN-91. The scope of the volumes is interdisciplinary, ranging from mathematics and engineering to cognitive sciences and biology. European research is well represented. Volume 1 contains all the orally presented papers, including both invited talks and submitted papers. Volume 2 contains the plenary talks and the poster presentations.

Neural Networks

  • 1st Edition
  • June 28, 2014
  • E. Gelenbe
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 0 9 - 5
The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoretical developments. Each paper is largely self-contained and includes a complete bibliography.The methodological part of the book contains two papers on learning, one paper which presents a computational model of intracortical inhibitory effects, a paper presenting a new development of the random neural network, and two papers on associative memory models. The applications and examples portion contains papers on image compression, associative recall of simple typed images, learning applied to typed images, stereo disparity detection, and combinatorial optimisation.

Artificial Neural Networks, 2

  • 1st Edition
  • June 28, 2014
  • I. Aleksander + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 8 0 6 - 1
This two-volume proceedings compilation is a selection of research papers presented at the ICANN-92. The scope of the volumes is interdisciplinary, ranging from the minutiae of VLSI hardware, to new discoveries in neurobiology, through to the workings of the human mind. USA and European research is well represented, including not only new thoughts from old masters but also a large number of first-time authors who are ensuring the continued development of the field.

Neurobionics

  • 1st Edition
  • October 22, 2013
  • H.-W. Bothe + 2 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 1 5 6 - 7
The goal of neurobionics is to elaborate methods for the repairment and substitution of impaired functions of the human nervous system. This publication contains contributions from internationally recognized scientists exploring the structure of this novel interdisciplinary research field. The structure consists of theoretical sciences (philosophy, mathematics, neuroinformatics, computational neuroscience), basic biological sciences (molecular biology, cell biology, biological network neuroscience, neurophysiology), technical engineering (microelectronics, micromechanics, robotics, microsystems), and clinical neurosciences (neurodiagnostics, neurology, neurosurgery, neurorehabilitation). It is hoped the book indicates that a new kind of partnership across these various disciplines is mandatory if emerging problems in the field are to be solved. It also aims to set the coordinates for an international and interdisciplinary research field dealing with a subject intrinsic to man's mind and its biological carrier which may be partially replaced by artificial means in the future.

Neural Networks and Genome Informatics

  • 1st Edition
  • Volume 1
  • December 2, 2012
  • C.H. Wu + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 3 7 3 7 - 5
This book is a comprehensive reference in the field of neural networks and genome informatics. The tutorial of neural network foundations introduces basic neural network technology and terminology. This is followed by an in-depth discussion of special system designs for building neural networks for genome informatics, and broad reviews and evaluations of current state-of-the-art methods in the field. This book concludes with a description of open research problems and future research directions.

Neural Networks in Finance

  • 1st Edition
  • December 22, 2004
  • Paul D. McNelis
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 7 9 6 5 - 1
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong.

Computing the Brain

  • 1st Edition
  • April 2, 2001
  • Michael A. Arbib + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 2 9 7 5 - 2
Computing the Brain provides readers with an integrated view of current informatics research related to the field of neuroscience. This book clearly defines the new work being done in neuroinformatics and offers information on resources available on the Web to researchers using this new technology. It contains chapters that should appeal to a multidisciplinary audience with introductory chapters for the nonexpert reader. Neuroscientists will find this book an excellent introduction to informatics technologies and the use of these technologies in their research. Computer scientists will be interested in exploring how these technologies might benefit the neuroscience community.

Kohonen Maps

  • 1st Edition
  • July 2, 1999
  • E. Oja + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 3 5 2 9 - 6
The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.