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Libros sobre el tema "Bayesian classification"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 20 de febrero de 2023

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1

T, Denison David G., ed. Bayesian methods for nonlinear classification and regression. Chichester, England: Wiley, 2002.

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2

PAC-Bayesian supervised classification: The thermodynamics of statistical learning. Beachwood, Ohio: Institute of Mathematical Statistics, 2007.

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3

Frey, Brendan J. Bayesian networks for pattern classification, data compression, and channel coding. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1997.

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4

Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Toronto: University of Toronto, 1997.

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5

Press, S. James. Bayesian statistics: Principles, models, and applications. New York: Wiley, 1989.

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6

Wang, Jun. A Bayesian classifier based on a deterministic annealing neural network for aircraft fault classification. Wright-Patterson AFB, OH: Human Resources Directorate, Logistics Research Division, U.S. Air Force Armstrong Laboratory, 1997.

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7

Abkar, Ali Akbar. Likelihood-based segmentation and classification of remotely sensed images: A Bayesian optimization approach for combining RS and GIS. Enschede, The Netherlands: International Institute for Aerospace Survey and Earth Sciences, 1999.

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8

John, Stutz, Cheeseman Peter y Ames Research Center. Artificial Intelligence Research Branch., eds. Bayesian classification theory. Moffett Field, CA: NASA Ames Research Center, Artificial Intelligence Research Branch, 1991.

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9

Dalton, Lori A. y Edward R. Dougherty. Optimal Bayesian Classification. SPIE, 2020.

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10

Dalton, Lori A. y Edward R. Dougherty. Optimal Bayesian Classification. SPIE, 2020. http://dx.doi.org/10.1117/3.2540669.

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11

Li, Longhai. Bayesian classification and regression with high dimensional features. 2007, 2007.

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12

Krishnamurthy, Vikram, Ba-Ngu Vo y Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management. Wiley & Sons, Incorporated, John, 2012.

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13

Zdziarski, Jonathan A. Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification. No Starch Press, Incorporated, 2005.

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14

Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification. No Starch Press, 2005.

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15

Krishnamurthy, Vikram, Ba-Ngu Vo y Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Limited, John, 2020.

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16

Krishnamurthy, Vikram, Ba-Ngu Vo y Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Incorporated, John, 2012.

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17

Krishnamurthy, Vikram, Ba-Ngu Vo y Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Incorporated, John, 2012.

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18

Krishnamurthy, Vikram, Ba-Ngu Vo y Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Incorporated, John, 2012.

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19

Butz, Martin V. y Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.
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20

Kockelman, Paul. Algorithms, Agents, and Ontologies. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190636531.003.0007.

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This chapter details the inner workings of spam filters, algorithmic devices that separate desirable messages from undesirable messages. It argues that such filters are a particularly important kind of sieve insofar as they readily exhibit key features of sieving devices in general, and algorithmic sieving in particular. More broadly, it describes the relation between ontology (assumptions that drive interpretations) and inference (interpretations that alter assumptions) as it plays out in the classification and transformation of identities, types, or kinds. Focusing on the unstable processes whereby identifying algorithms, identified types, and evasive transformations are dynamically coupled over time, it also theorizes various kinds of ontological inertia and highlights various kinds of algorithmic ineffability. Finally, it shows how similar issues underlie a much wider range of processes, such as the Turing Test, Bayesian reasoning, and machine learning more generally.
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