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Books on the topic 'Statistics|Artificial intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Hand, D. J., ed. Artificial Intelligence Frontiers in Statistics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2.

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2

E, Nicholson Ann, ed. Bayesian artificial intelligence. 2nd ed. CRC Press, 2011.

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3

Kharin, Yurij. Robustness in Statistical Pattern Recognition. Springer Netherlands, 1996.

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4

Marwala, Tshilidzi. Economic Modeling Using Artificial Intelligence Methods. Springer London, 2013.

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5

Kruse, Rudolf. Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Springer Berlin Heidelberg, 2013.

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6

Rzempoluck, Edward J. Neural Network Data Analysis Using SimulnetTM. Springer New York, 1998.

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7

Berthold, M. Intelligent data analysis: An introduction. Springer, 1999.

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8

Goodman, I. R. Mathematics of Data Fusion. Springer Netherlands, 1997.

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9

Berthold, M. Guide to intelligent data analysis: How to intelligently make sense of real data. Springer, 2010.

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10

Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. Springer New York, 1995.

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11

Bustince, Humberto. Aggregation Functions in Theory and in Practise: Proceedings of the 7th International Summer School on Aggregation Operators at the Public University of Navarra, Pamplona, Spain, July 16-20, 2013. Springer Berlin Heidelberg, 2013.

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12

Powell, Warren B. Optimal learning. Wiley, 2012.

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13

Tzeng, Chun-Hung. A Theory of Heuristic Information in Game-Tree Search. Springer Berlin Heidelberg, 1988.

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14

Emmert-Streib, Frank. Information Theory and Statistical Learning. Springer US, 2009.

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15

Schlesinger, Michail I. Ten Lectures on Statistical and Structural Pattern Recognition. Springer Netherlands, 2002.

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16

Mari, Jean-François. Probabilistic and Statistical Methods in Computer Science. Springer US, 2001.

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17

Serdobolskii, V. Multivariate Statistical Analysis: A High-Dimensional Approach. Springer Netherlands, 2000.

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18

Tantar, Emilia. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Springer Berlin Heidelberg, 2013.

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19

service), SpringerLink (Online, ed. Criminal Justice Forecasts of Risk: A Machine Learning Approach. Springer New York, 2012.

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20

Trevor, Hastie, Tibshirani Robert, and SpringerLink (Online service), eds. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag New York, 2009.

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21

Lavrač, Nada. Intelligent Data Analysis in Medicine and Pharmacology. Springer US, 1997.

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22

Skousen, Royal. Analogy and Structure. Springer Netherlands, 1992.

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23

Erickson, Gary J. Maximum Entropy and Bayesian Methods: Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis. Springer Netherlands, 1998.

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24

Corfield, David. Foundations of Bayesianism. Springer Netherlands, 2001.

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25

Nguyen, Hung T. Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering. Springer-Verlag Berlin Heidelberg, 2012.

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26

Prague Conference on Information Theory, Statistical Decision Functions, Random Processes (11th 1990). Information theory, statistical decision, functions, random processes: Transactions of the 11th Prague Conference held from August 27 to 31, 1990. Kluwer Academic Publishers, 1992.

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27

Nielsen, Søren S. Programming Languages and Systems in Computational Economics and Finance. Springer US, 2002.

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28

Jebara, Tony. Machine Learning: Discriminative and Generative. Springer US, 2004.

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29

W, Mielke Paul, ed. Statistical Mining and Data Visualization in Atmospheric Sciences. Springer US, 2000.

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30

Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Springer US, 2001.

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31

Hristea, Florentina T. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection. Springer Berlin Heidelberg, 2013.

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32

Ristic, Branko. Particle Filters for Random Set Models. Springer New York, 2013.

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33

Neal, Radford M. Bayesian learning for neural networks. Springer, 1996.

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34

Liu, Huan. Feature Extraction, Construction and Selection: A Data Mining Perspective. Springer US, 1998.

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35

Neal, Radford M. Bayesian learning for neural networks. University of Toronto, Dept. of Computer Science, 1995.

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36

Automatic ambiguity resolution in natural language processing: An empirical approach. Springer, 1996.

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37

Kjaerulff, Uffe B. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. 2nd ed. Springer New York, 2013.

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38

Lapko, V. A. Neparametricheskie kollektivy reshai︠u︡shchikh pravil. "Nauka", 2002.

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39

MLMI 2010 (2010 Beijing, China). Machine learning in medical imaging: First International Workshop, MLMI 2010, held in conjunction with MICCAI 2010, Beijing, China, September 20, 2010 : proceedings. Springer, 2010.

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40

Aertsen, Ad. Information Processing in the Cortex: Experiments and Theory. Springer Berlin Heidelberg, 1992.

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41

Studený, Milan. Probabilistic conditional independence structures. Springer, 2005.

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42

Aggarwal, Charu C. Outlier Analysis. Springer New York, 2013.

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43

Schütze, Oliver. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Springer Berlin Heidelberg, 2013.

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44

A, Gale William, AT & T Bell Laboratories., and Workshop on Artificial Intelligence and Statistics (1st : 1985 : Princeton, N.J.), eds. Artificial intelligence and statistics. Addison-Wesley Pub. Co., 1986.

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45

(Editor), Tommi Jaakkola, and Thomas Richardson (Editor), eds. Artificial Intelligence and Statistics 1999 - 2001. Morgan Kaufmann, 2001.

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46

J, Hand D., ed. Artificial intelligence frontiers in statistics: AI and statistics III. Chapman & Hall, 1993.

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47

Hand, David J. Artificial Intelligence Frontiers in Statistics: Al and Statistics III. Chapman & Hall/CRC, 1992.

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48

Learning from Data: Artificial Intelligence and Statistics V. Springer, 2011.

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49

H, Fisher Douglas, Lenz Hans-Joachim, and Workshop on Artificial Intelligence and Statistics (5th : 1995 : Ft. Lauderdale, Fla.), eds. Learning from data: Artificial intelligence and statistics V. Springer, 1996.

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50

(Editor), Doug Fisher, and Hans-J. Lenz (Editor), eds. Learning from Data: Artificial Intelligence and Statistics V (Lecture Notes in Statistics). Springer, 1996.

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