1 edition of Computational Models of Learning found in the catalog.
|Statement||edited by Leonard Bolc|
|Series||Symbolic Computation, Artificial Intelligence, Symbolic computation|
|The Physical Object|
|Format||[electronic resource] /|
|Pagination||1 online resource (IX, 208 pages 34 illustrations).|
|Number of Pages||208|
Sequencing: Putting order to the things that need to happen Decomposition: Breaking Complex problems into discrete parts Algorithms: Crafting repeatable models of how to solve a problem Pattern-Spotting and -Making: Seeing Clearly how Things Relate and Repeat Loops: Programming Sequences to Repeat over and over Conditionals: One thing happens depending on the situations of other things. The focus of this book is on three influential cognitive motives: achievement, affiliation, and power motivation. Incentive-based theories of achievement, affiliation and power motivation are the basis for competence-seeking behaviour, relationship-building, leadership, and resource-controlling Computational Models of Motivation for Game. Find many great new & used options and get the best deals for Psychology of Learning and Motivation, Vol. Computational Models of Learning in Simple Neural Systems (, Paperback) at the best online prices at eBay! Free shipping for many products!
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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 Computational Models of Learning book 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 Computational Models of Learning book different aspects of computational modeling efforts in psychology and neuroscience.
Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, /5(3). 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: computational models are to explain animal and human cognition, they will have to perform feats of intelligence. Machine learning and AI more broadly are therefore key disciplines that provide the theoretical and technological foundation for cognitive computational : Nikolaus Kriegeskorte, Pamela K.
Douglas. Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed inthe authors complete a computational experiment which examines the role of cognitive retraining on student Size: KB.
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. Algorithmic learning theory, from the work of E. Mark Gold; Online machine learning, from the work of Nick Littlestone.
While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms.
A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation.
The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem.
Andreas Heinz is Director and Chair of the Department of Psychiatry and Psychotherapy at the Charité— Universitätsmedizin Berlin. Book Description. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods.
The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation.
Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence. Generally, computational intelligence is a set of nature-inspired computational methodologies.
Structural changes accompany memory formation and learning, and are induced by neurogenesis, neurodegeneration and brain injury such as stroke. Exploring the role of structural plasticity in the brain can be greatly assisted by mathematical and computational models, as they enable us to bridge the gap between system-level dynamics and lower.
Abstract. In temporal information processing, both the cerebellum and the basal ganglia play essential roles.
In particular, for interval timing learning, the cerebellum exhibits temporally localized activity around the onset of the unconditioned stimulus, whereas the basal ganglia represents the passage of time by their ramping-up activity from the onset of the conditioned stimulus to that of Author: Ohki Katakura, Tadashi Yamazaki, Tadashi Yamazaki.
"Computer metaphor" Computational theory of mind is not the same as the computer metaphor, comparing the mind to a modern-day digital computer. Computational theory just uses some of the same principles as those found in digital computing. While the computer metaphor draws an analogy between the mind as software and the brain as hardware, CTM is the claim that the mind is a computational.
This book explores unintentional learning from an information-processing perspective. What do people learn when they do not know that they are learning. Until recently all of the work in the area of implicit learning focused on empirical questions and methods.
In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. Computational cognitive modeling aims to understand behavioral data and the mind and brain, more generally, by building computational models of the cognitive processes that produce the data.
This course introduces the goals, philosophy, and technical concepts behind computational. As far as an online introduction, it would be hard to beat Scholarpedia, started by leading computational neuroscientist (and now founder of Brain Corporation) Eugene. This book was sent to me by CRC Press for review for CHANCE.
I read it over a few mornings while [confined] at home and found it much more computational than the sense that the authors go quite thoroughly into the construction of standard learning procedures, including home-made R codes that obviously help in understanding the nitty-gritty of these procedures, what .