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Computational Learning And Probabilistic Reasoning

techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational;

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Probabilistic Reasoning in Intelligent Systems

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational;

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Knowledge Representation Reasoning & The

-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP;

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Advances in Artificial Intelligence IBERAMIA 2016

in the following topical sections: knowledge engineering, knowledge representation and probabilistic reasoning; agent technology and multi-agent;

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Foundations of the Formal Sciences VI: Probabilistic Reasoning and Reasoning with Probabilities

Probabilistic methods are increasingly becoming an important tool in a variety of disciplines including computer science, mathematics;

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Advances in Machine Learning and Computational Intelligence

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence;

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Uncertainty Reasoning for the Semantic Web III

reviewed and selected from numerous submissions. The papers included in this volume are organized in topical sections on probabilistic and Dempster;

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Artificial Intelligence

. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural;

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Artificial Intelligence

. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural;

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Artificial Intelligence

. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural;

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Artificial Intelligence

. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural;

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Statistics in Early Childhood and Primary Education

account of current best practices, challenges, and issues, and of future trends and directions in early statistical and probabilistic learning;

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Probabilistic Graphical Models

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available;

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

; logics; orthopairs; possibilistic networks; and probabilistic logics, probabilistic reasoning.;

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Stochastic Models of Uncertainties in Computational Mechanics

Stochastic Models of Uncertainties in Computational Mechanics presents the main concepts, formulations, and recent advances in the use of a;

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Probabilistic Reasoning in Multiagent Systems

in multiple cooperative agents, and effective, exact and distributed probabilistic inference.;

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Probabilistic Reasoning in Multiagent Systems

in multiple cooperative agents, and effective, exact and distributed probabilistic inference.;

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Data-Driven Computational Neuroscience

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain;

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Visual Analysis of Behaviour

This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives;

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Teaching and Learning Stochastics

This book presents a collection of selected papers that represent the current variety of research on the teaching and learning;

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Probabilistic Methods for Financial and Marketing Informatics

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to;

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Introduction to Artificial Intelligence

, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper;

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Artificial Intelligence

modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These;

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Artificial Intelligence

modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These;

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Similarity and Analogical Reasoning

developments in research and theory from psychological, computational, and educational perspectives, and considers their implications for learning and;

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Quantitative Reasoning

, symbolic, verbal, and graphical forms of functions and relations; graphs and pictorial representations of data; interpretations of probabilistic;

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Case-Based Reasoning Research and Development

Learning, CBR in the Health Sciences, Computational Analogy, and Process-Oriented CBR.;

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