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Libros sobre el tema "Automaton inference"

Autor: Grafiati
Publicado: 1 de junio de 2024
Última modificación: 31 de julio de 2025

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1

Lee, Won Don. Probabilistic inference. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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2

Lee, Won Don. Probabilistic inference: Theory and practice. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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3

author, Kohlas Jürg 1939, ed. Generic Inference: A Unifying Theory for Automated Reasoning. Wiley, 2011.

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4

Farreny, Henri. AI and expertise: Heuristic search, inference engines, automatic proving. E. Horwood, 1989.

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5

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. 
 The textbook in the field of training "Computer Science and Computer Engineering"
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6

Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th
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7

Higuera, Colin De La. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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8

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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9

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2014.

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10

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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11

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2011.

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12

Farreny, Henri. AI & Expertise: Heuristic Search, Inference Engines, Automatic Proving. Prentice Hall, 1989.

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13

Inference in Hidden Markov Models. Springer London, Limited, 2006.

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14

Cappé, Olivier, Eric Moulines, and Tobias Ryden. Inference in Hidden Markov Models. Springer, 2010.

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15

Zhou, Xuefeng. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Nature, 2020.

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16

Wu, Hongmin, Zhihao Xu, Shuai Li, Xuefeng Zhou, and Juan Rojas. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore Pte. Limited, 2020.

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17

Wu, Hongmin, Zhihao Xu, Shuai Li, Xuefeng Zhou, and Juan Rojas. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore Pte. Limited, 2020.

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18

Hadjicostis, Christoforos N. Estimation and Inference in Discrete Event Systems: A Model-Based Approach with Finite Automata. Springer International Publishing AG, 2020.

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19

Hadjicostis, Christoforos N. Estimation and Inference in Discrete Event Systems: A Model-Based Approach with Finite Automata. Springer, 2019.

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20

Data Science: Algorithm Analysis, Approximation, Automata, Bayesian Inference, Statistics, Science,TR6 Binary Tree, Bioinformatics, Algorithms, Complexity Theory, Computability and Research. Independently Published, 2022.

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21

Hilton, Denis. Social Attribution and Explanation. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.33.

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Attribution processes appear to be an integral part of human visual perception, as low-level inferences of causality and intentionality appear to be automatic and are supported by specific brain systems. However, higher-order attribution processes use information held in memory or made present at the time of judgment. While attribution processes about social objects are sometimes biased, there is scope for partial correction. This chapter reviews work on the generation, communication, and interpretation of complex explanations, with reference to explanation-based models of text understanding t
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22

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelli
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23

Hatlebrekke, Kjetil Anders. The Problem of Secret Intelligence. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9780748691838.001.0001.

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Why is intelligence so hard to define? Why is there no systematic or adequate theory of intelligence? This book argues that classic intelligence production has been premised on an ill-founded belief in an automatic inference between history and the future, and that the lack of a working theory has exacerbated this problem. The book uses classic cases of intelligence failure to demonstrate how this problem creates a restricted language in intelligence communities that undermines threat perception. From these cases it concludes that intelligence needs to be re-thought, and argues that good intel
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