Author: Michael A. Arbib
Publisher: Springer Science & Business Media
ISBN: 9780387968933
Category : Computers
Languages : en
Pages : 296
Book Description
The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.
Dynamic Interactions in Neural Networks: Models and Data
Author: Michael A. Arbib
Publisher: Springer Science & Business Media
ISBN: 9780387968933
Category : Computers
Languages : en
Pages : 296
Book Description
The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.
Publisher: Springer Science & Business Media
ISBN: 9780387968933
Category : Computers
Languages : en
Pages : 296
Book Description
The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.
Dynamic Interactions in Neural Networks
Author: Shun'ichi Amari
Publisher:
ISBN: 9787506212717
Category : Neural computers
Languages : en
Pages : 280
Book Description
Publisher:
ISBN: 9787506212717
Category : Neural computers
Languages : en
Pages : 280
Book Description
Dynamic Interactions in Neural Networks
Author: Michael A Arbib
Publisher:
ISBN: 9781461245377
Category :
Languages : en
Pages : 292
Book Description
Publisher:
ISBN: 9781461245377
Category :
Languages : en
Pages : 292
Book Description
Survival and Event History Analysis
Author: Odd Aalen
Publisher: Springer Science & Business Media
ISBN: 038768560X
Category : Mathematics
Languages : en
Pages : 550
Book Description
The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.
Publisher: Springer Science & Business Media
ISBN: 038768560X
Category : Mathematics
Languages : en
Pages : 550
Book Description
The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.
Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLABĀ®.
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLABĀ®.
Current Catalog
Author: National Library of Medicine (U.S.)
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 1024
Book Description
First multi-year cumulation covers six years: 1965-70.
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 1024
Book Description
First multi-year cumulation covers six years: 1965-70.
Signal Processing, Sensor Fusion, and Target Recognition VI.
Author: Ivan Kadar
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 600
Book Description
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 600
Book Description
Connectionistic Problem Solving
Author: HAMPSON
Publisher: Springer Science & Business Media
ISBN: 1468467700
Category : Computers
Languages : en
Pages : 277
Book Description
1. 1 The problem and the approach The model developed here, which is actually more a collection of com ponents than a single monolithic structure, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasingly sophisticated behavior. The initial chapters develop the basic components of the sys tem at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more con cerned with the problems of assembling sequences of actions in order to achieve a given goal state. The model is referred to as connectionistic rather than neural, be cause, while the basic components are neuron-like, there is only limited commitment to physiological realism. Consequently the neuron-like ele ments are referred to as "nodes" rather than "neurons". The model is directed more at the behavioral level, and at that level, numerous con cepts from animal learning theory are directly applicable to connectionis tic modeling. An attempt to actually implement these behavioral theories in a computer simulation can be quite informative, as most are only partially specified, and the gaps may be apparent only when actual ly building a functioning system. In addition, a computer implementa tion provides an improved capability to explore the strengths and limita tions of the different approaches as well as their various interactions.
Publisher: Springer Science & Business Media
ISBN: 1468467700
Category : Computers
Languages : en
Pages : 277
Book Description
1. 1 The problem and the approach The model developed here, which is actually more a collection of com ponents than a single monolithic structure, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasingly sophisticated behavior. The initial chapters develop the basic components of the sys tem at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more con cerned with the problems of assembling sequences of actions in order to achieve a given goal state. The model is referred to as connectionistic rather than neural, be cause, while the basic components are neuron-like, there is only limited commitment to physiological realism. Consequently the neuron-like ele ments are referred to as "nodes" rather than "neurons". The model is directed more at the behavioral level, and at that level, numerous con cepts from animal learning theory are directly applicable to connectionis tic modeling. An attempt to actually implement these behavioral theories in a computer simulation can be quite informative, as most are only partially specified, and the gaps may be apparent only when actual ly building a functioning system. In addition, a computer implementa tion provides an improved capability to explore the strengths and limita tions of the different approaches as well as their various interactions.
NeuralSource
Author: Philip D. Wasserman
Publisher: Van Nostrand Reinhold Company
ISBN:
Category : Computers
Languages : en
Pages : 1032
Book Description
Derived from the database Neural Base (still available at $495.00), this bibliography, covering more than 4,000 references, is an important collection of research information. Extensive annotations have been added to approximately 75% of the entries in the print version. Periodicals, private reports, and books are included. Indexed by author, keyword, and publication. Neurons were slacking off when A mathematical theory... was indexed under "A". Annotation copyrighted by Book News, Inc., Portland, OR
Publisher: Van Nostrand Reinhold Company
ISBN:
Category : Computers
Languages : en
Pages : 1032
Book Description
Derived from the database Neural Base (still available at $495.00), this bibliography, covering more than 4,000 references, is an important collection of research information. Extensive annotations have been added to approximately 75% of the entries in the print version. Periodicals, private reports, and books are included. Indexed by author, keyword, and publication. Neurons were slacking off when A mathematical theory... was indexed under "A". Annotation copyrighted by Book News, Inc., Portland, OR
Recent Advances on Soft Computing and Data Mining
Author: Rozaida Ghazali
Publisher: Springer Nature
ISBN: 3031669657
Category : Data mining
Languages : en
Pages : 452
Book Description
This book explores methods for leveraging data to create innovative solutions that offer significant and meaningful value. It provides practical insights into the concepts and techniques essential for maximizing the outcomes of large-scale research and data mining projects. Readers are guided through analytical thinking processes, addressing challenges in deciphering complex data systems and deriving commercial value from the data. Soft computing and data mining, also known as data-driven science, encompass a diverse range of interdisciplinary scientific methods and processes. The proceedings of "Recent Advances on Soft Computing and Data Mining" provide comprehensive knowledge to address various challenges encountered in complex systems. By integrating practices and applications from both domains, it offers a robust framework for tackling these issues. To excel in data-driven ecosystems, researchers, data analysts, and practitioners must carefully select the most suitable approaches and tools. Understanding the design choices and options available is essential for appreciating the underlying concepts, tools, and techniques utilized in these endeavors.
Publisher: Springer Nature
ISBN: 3031669657
Category : Data mining
Languages : en
Pages : 452
Book Description
This book explores methods for leveraging data to create innovative solutions that offer significant and meaningful value. It provides practical insights into the concepts and techniques essential for maximizing the outcomes of large-scale research and data mining projects. Readers are guided through analytical thinking processes, addressing challenges in deciphering complex data systems and deriving commercial value from the data. Soft computing and data mining, also known as data-driven science, encompass a diverse range of interdisciplinary scientific methods and processes. The proceedings of "Recent Advances on Soft Computing and Data Mining" provide comprehensive knowledge to address various challenges encountered in complex systems. By integrating practices and applications from both domains, it offers a robust framework for tackling these issues. To excel in data-driven ecosystems, researchers, data analysts, and practitioners must carefully select the most suitable approaches and tools. Understanding the design choices and options available is essential for appreciating the underlying concepts, tools, and techniques utilized in these endeavors.