6 edition of Computational models of learning found in the catalog.
Includes bibliographies and index.
|Statement||edited by Leonard Bolc ; with contributions by G.L. Bradshaw ... [et al.].|
|Contributions||Bolc, Leonard, 1934-, Bradshaw, G. L.|
|LC Classifications||Q325 .C626 1987|
|The Physical Object|
|Pagination||vii, 208 p. :|
|Number of Pages||208|
|LC Control Number||87016527|
Summary. Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book. Good theoretical models of these systems ultimately may have to grapple with learning or evolutionary adaptation. Second, machine-learning methods can be used for knowledge acquisition, which is generally the most time-consuming part of a computational project.
Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.4/5(1). The introduction of a mathematical and computational framework within which to analyze the interplay between language learning and language evolution. The nature of the interplay between language learning and the evolution of a language over generational time is subtle. We can observe the learning of language by children and marvel at the phenomenon of language acquisition; the evolution of a.
Computational models of increasing complexity have been proposed for the molecular mechanism of these rhythms, which occur spontaneously with a period on the order of 24 h. We show that deterministic models for circadian rhythms in Drosophila account for a variety of dynamical properties, such as phase shifting or long-term suppression by light. ISBN: OCLC Number: Description: xiv, pages: illustrations. Contents: Quantitative modeling of synaptic plasticity / David C. Tam and Donald H. Perkel --Computational capabilities of single neurons: relationship to simple forms of associative and nonassociative learning in Aplysia / John H. Byrne, Kevin J. Gingrich and Douglas .
Acoustics, vibrations, and rotating machines
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Authorizing extension of time limitation for a FERC-issued hydroelectric license
In recent years, machine learning has emerged as a significant area of research in artificial intelligence and cognitive science.
At present Computational Models of Learning (Symbolic Computation): Leonard Bolc: : BooksCited by: Computational Models of Learning supplements these contributions and is a collection of more extensive essays.
These essays provide the reader with an increased knowledge of carefully selected problems of machine learning. The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age.
However Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence: Lakshmi Gogate, George Hollich: : BooksCited by: 2. Computational Models of Scientific Discovery and Theory Formation (Morgan Kaufman Series in Machine Learning) by Jeff Shrager (Author), Pat Langley (Editor)Cited by: Computational Models of Learning and Beyond: Symmetries of Associative Learning: /ch The authors propose in this chapter to use abstract algebra to unify different models of theories of associative learning -- as complementary to currentAuthor: Eduardo Alonso, Esther Mondragón.
A comprehensive introduction to the world of brain and behavior computational models. This unique resource provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience.
Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, Computational models of learning book. Cognitive Models of Learning. Cite this entry as: () Computational Models of Learning. In: Seel N.M. (eds) Encyclopedia of the Sciences of Learning.
Standard computational models assume a discrete time paradigm. A mathematical object representing a question that computers might be able to solve. Pavlovian Creatures: A concept of evolutionary agency by Daniel Dennett in which creatures have a nervous system, and stimulus-response learning is by: 3.
Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.
Computational Quantum Chemistry removes much of the mystery of modern computer programs for molecular orbital calculations by showing how to develop Excel spreadsheets to perform model calculations and investigate the properties of basis sets.
Using the book together with the CD-ROM provides a unique interactive learning tool. Computational models of learning. Berlin ; New York: Springer-Verlag, © (OCoLC) Online version: Computational models of learning. Berlin ; New York: Springer-Verlag, © (OCoLC) Document Type: Book: All Authors / Contributors: Leonard Bolc; G L Bradshaw.
The authors treat computational methods, including dynamic simulation (Monte Carlo methods), knowledge-based models (semantic networks, frame systems, and rule-based systems), and machine learning (connectionism, rule induction, and genetic algorithms), as a single broad-based class of research tools and develop a framework for incorporating.
Production System Models of Learning and Development is included in the series Computational Models of Cognition and Perception, edited by Jerome A. Feldman, Patrick J. Hayes, and David art. Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning.
Blending together. CHAPTER 1 Computational Models of Concept Learning DOUG FISHER MICHAEL PAZZANI 1. I n t r o d u c t i o n The success of an intelligent agent, whether human or machine, depends critically on an ability to adapt to the environment through learning, Figure 1 (Dietterich, Clarkson, Dromey, & London, ) illustrates that learning organizes experiences in a manner that ideally improves Cited by: Abstract.
This chapter presents a general overview on computational models of the heart. It focuses on mathematical modeling of the various physiological aspects involved in cardiac function, namely anatomy, electrophysiology, biomechanics, fluid dynamics and fluid structure interaction.
In a computational implementation of this principle with a spiking neural network model of the hippocampus and prefrontal cortex, simulations demonstrated that the supervised learning of sequential patterns in the cortex benefits from the sleep-dependent unsupervised learning in the hippocampus in a forward predictive task (Lerner, b).Cited by: 1.
Teachers should set an example of learning by modeling their own understanding, learning, and progress in computational thinking. Especially in the early stages, they should also model the computational thinking process for students so they understand what the learning, reflection, and revision look like (Highfield, ).Author: Anne Ottenbreit-Leftwich, Royce Kimmons.
Theory and Applications of Computational Chemistry: The First Forty Years is a collection of articles on the emergence of computational chemistry.
It shows the enormous breadth of theoretical and computational chemistry today and establishes how theory and computation have become increasingly linked as methodologies and technologies have advanced.
The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning.
Jarosz then contrasts three computational models of this process: Stochastic OT, MLG (Maximum Likelihood Learning of Lexicons and Grammars) and HG (Harmonic Grammar). He states that all three models incorporate the same core claims of the frequency hypothesis regarding the primacy of markedness. This may be by: Summary.
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules.The MIT Press is a leading publisher of books and journals at the intersection of science, technology, and the arts.
A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia (deciding how to manipulate working memory and perform processing).
Although many computational models of working memory have been developed Cited by: