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Journal articles on the topic 'Information-Based Complexity'

Author: Grafiati

Published: 21 September 2024

Last updated: 31 July 2025

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1

Wozniakowski, H. "Information-Based Complexity." Annual Review of Computer Science 1, no. 1 (1986): 319–80. http://dx.doi.org/10.1146/annurev.cs.01.060186.001535.

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2

Packel, Edward W., and J. F. Traub. "Information-based complexity." Nature 328, no. 6125 (1987): 29–33. http://dx.doi.org/10.1038/328029a0.

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3

Heinrich, Stefan, and Jörg-Detlef Kern. "Parallel information-based complexity." Journal of Complexity 7, no. 4 (1991): 339–70. http://dx.doi.org/10.1016/0885-064x(91)90024-r.

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4

Kon, Mark A. "Book Review: Information-based complexity." Bulletin of the American Mathematical Society 21, no. 2 (1989): 332–40. http://dx.doi.org/10.1090/s0273-0979-1989-15851-5.

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5

Traub, J. F., and H. Wo\'zniakowski. "Perspectives on Information-Based Complexity." Bulletin of the American Mathematical Society 26, no. 1 (1992): 29–53. http://dx.doi.org/10.1090/s0273-0979-1992-00240-9.

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6

Lui, Leong Ting, Germán Terrazas, Hector Zenil, Cameron Alexander, and Natalio Krasnogor. "Complexity Measurement Based on Information Theory and Kolmogorov Complexity." Artificial Life 21, no. 2 (2015): 205–24. http://dx.doi.org/10.1162/artl_a_00157.

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In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannon's information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.

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7

Parlett, Beresford N. "Some basic information\\ on information-based complexity theory." Bulletin of the American Mathematical Society 26, no. 1 (1992): 3–29. http://dx.doi.org/10.1090/s0273-0979-1992-00239-2.

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8

Orme, Anthony Mark, Haining Yao, and Letha H. Etzkorn. "Complexity metrics for ontology based information." International Journal of Technology Management 47, no. 1/2/3 (2009): 161. http://dx.doi.org/10.1504/ijtm.2009.024120.

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9

Packel, Edward W., and Henryk Woźniakowski. "Recent developments in information-based complexity." Bulletin of the American Mathematical Society 17, no. 1 (1987): 9–37. http://dx.doi.org/10.1090/s0273-0979-1987-15511-x.

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10

Galas, David J., Matti Nykter, Gregory W. Carter, Nathan D. Price, and Ilya Shmulevich. "Biological Information as Set-Based Complexity." IEEE Transactions on Information Theory 56, no. 2 (2010): 667–77. http://dx.doi.org/10.1109/tit.2009.2037046.

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11

Woźniakowski, H. "A survey of information-based complexity." Journal of Complexity 1, no. 1 (1985): 11–44. http://dx.doi.org/10.1016/0885-064x(85)90020-2.

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12

Woźniakowski, H. "Probabilistic setting of information-based complexity." Journal of Complexity 2, no. 3 (1986): 255–69. http://dx.doi.org/10.1016/0885-064x(86)90005-1.

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13

Mathé, P. "s-Numbers in information-based complexity." Journal of Complexity 6, no. 1 (1990): 41–66. http://dx.doi.org/10.1016/0885-064x(90)90011-2.

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14

Traub, J. F., and H. Woźniakowski. "Information-Based complexity: New questions for mathematicians." Mathematical Intelligencer 13, no. 2 (1991): 34–43. http://dx.doi.org/10.1007/bf03024085.

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15

Nemirovsky, A. S. "Information-based complexity of linear operator equations." Journal of Complexity 8, no. 2 (1992): 153–75. http://dx.doi.org/10.1016/0885-064x(92)90013-2.

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16

Cheung, Karen S. K., and Douglas Vogel. "Complexity Reduction in Lattice-Based Information Retrieval." Information Retrieval 8, no. 2 (2005): 285–99. http://dx.doi.org/10.1007/s10791-005-5663-y.

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17

Pozo, Jose M., Arjan J. Geers, Maria-Cruz Villa-Uriol, and Alejandro F. Frangi. "Flow complexity in open systems: interlacing complexity index based on mutual information." Journal of Fluid Mechanics 825 (July 21, 2017): 704–42. http://dx.doi.org/10.1017/jfm.2017.392.

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Flow complexity is related to a number of phenomena in science and engineering and has been approached from the perspective of chaotic dynamical systems, ergodic processes or mixing of fluids, just to name a few. To the best of our knowledge, all existing methods to quantify flow complexity are only valid for infinite time evolution, for closed systems or for mixing of two substances. We introduce an index of flow complexity coined interlacing complexity index (ICI), valid for a single-phase flow in an open system with inlet and outlet regions, involving finite times. ICI is based on Shannon’s

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18

Han, Cuize, and Ming Yuan. "Information based complexity for high dimensional sparse functions." Journal of Complexity 57 (April 2020): 101443. http://dx.doi.org/10.1016/j.jco.2019.101443.

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19

Tavares, Gabriela, and Panos Parpas. "On the information-based complexity of stochastic programming." Operations Research Letters 41, no. 6 (2013): 622–26. http://dx.doi.org/10.1016/j.orl.2013.08.011.

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20

Bonmati, Ester, Anton Bardera, Miquel Feixas, and Imma Boada. "Novel Brain Complexity Measures Based on Information Theory." Entropy 20, no. 7 (2018): 491. http://dx.doi.org/10.3390/e20070491.

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21

Krivovichev, Sergey V. "Information-based measures of structural complexity of crystals." Acta Crystallographica Section A Foundations and Advances 73, a2 (2017): C378. http://dx.doi.org/10.1107/s2053273317091951.

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22

Zhang, H. X., Y. S. Zhu, and Z. M. Wang. "Complexity measure and complexity rate information based detection of ventricular tachycardia and fibrillation." Medical & Biological Engineering & Computing 38, no. 5 (2000): 553–57. http://dx.doi.org/10.1007/bf02345752.

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23

Raginsky, Maxim, and Alexander Rakhlin. "Information-Based Complexity, Feedback and Dynamics in Convex Programming." IEEE Transactions on Information Theory 57, no. 10 (2011): 7036–56. http://dx.doi.org/10.1109/tit.2011.2154375.

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24

Novak, Erich, Ian H. Sloan, Joseph F. Traub, and Henryk Wozniakowski. "Frances Kuo Wins the 2014 Information-Based Complexity Prize." Journal of Complexity 30, no. 4 (2014): v. http://dx.doi.org/10.1016/s0885-064x(14)00056-9.

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25

Novak, Erich. "Nominations for 2016 Information-Based Complexity Young Researcher Award." Journal of Complexity 34 (June 2016): vii. http://dx.doi.org/10.1016/s0885-064x(16)30003-6.

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26

Drori, Yoel. "The exact information-based complexity of smooth convex minimization." Journal of Complexity 39 (April 2017): 1–16. http://dx.doi.org/10.1016/j.jco.2016.11.001.

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27

Le Yi Wang and Lin Lin. "Information-based complexity of uncertainty sets in feedback control." IEEE Transactions on Automatic Control 46, no. 4 (2001): 519–33. http://dx.doi.org/10.1109/9.917654.

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28

Dale, M. B., M. Anand, and R. E. Desrochers. "Measuring information-based complexity across scales using cluster analysis." Ecological Informatics 2, no. 2 (2007): 121–27. http://dx.doi.org/10.1016/j.ecoinf.2007.03.011.

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29

Milanese, M., and A. Vicino. "Information-Based Complexity and Nonparametric Worst-Case System Identification." Journal of Complexity 9, no. 4 (1993): 427–46. http://dx.doi.org/10.1006/jcom.1993.1028.

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30

Pavlenko, Yaryna, and Iryna Yurchak. "Information currency converter based on Telegram messenger." Computer systems and network 4, no. 1 (2022): 106–21. http://dx.doi.org/10.23939/csn2022.01.106.

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The work is dedicated to the development of a mobile chatbot containing an information currency converter, designed for use by a wide range of people. A chatbot is a subject-oriented text-based dialog interface that allows a user to perform a limited set of tasks: getting information about the current rate of currencies (USD or EUR) relative to the national currency and finding out the current rate of cryptocurrencies (Bitcoin, Ethereum, Litecoin) in dollars or euros. To achieve this goal, the selected subject area was analyzed and appropriate conclusions were made. A corresponding study of an

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31

Sun, Shuliang. "A New Information Hiding Method Based on Improved BPCS Steganography." Advances in Multimedia 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/698492.

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Bit-plane complexity segmentation (BPCS) steganography is advantageous in its capacity and imperceptibility. The important step of BPCS steganography is how to locate noisy regions in a cover image exactly. The regular method, black-and-white border complexity, is a simple and easy way, but it is not always useful, especially for periodical patterns. Run-length irregularity and border noisiness are introduced in this paper to work out this problem. Canonical Cray coding (CGC) is also used to replace pure binary coding (PBC), because CGC makes use of characteristic of human vision system. Conju

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32

Cheng, Ying, ZhiWei Guan, and HongLin Zhao. "Complexity metrics for auto fault diagnosis based on information entropy." IOP Conference Series: Materials Science and Engineering 392 (August 3, 2018): 062147. http://dx.doi.org/10.1088/1757-899x/392/6/062147.

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33

Wu, Xue. "Calculation of the Minimum Computational Complexity Based on Information Entropy." International Journal on Computational Science & Applications 2, no. 1 (2012): 73–82. http://dx.doi.org/10.5121/ijcsa.2012.2107.

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34

Jiang, Tianzi. "A parallel information-based complexity approach to visual surface reconstruction." International Journal of Computer Mathematics 70, no. 2 (1998): 165–77. http://dx.doi.org/10.1080/00207169808804744.

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35

Woźniakowski, Henryk. "Why does information-based complexity use the real number model?" Theoretical Computer Science 219, no. 1-2 (1999): 451–65. http://dx.doi.org/10.1016/s0304-3975(98)00300-4.

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36

Novak, Erich. "2017 Joseph F. Traub Information-Based Complexity Young Researcher Award." Journal of Complexity 39 (April 2017): vi. http://dx.doi.org/10.1016/s0885-064x(17)30019-5.

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37

Novak, Erich. "2018 Joseph F. Traub Information-Based Complexity Young Researcher Award." Journal of Complexity 44 (February 2018): v. http://dx.doi.org/10.1016/s0885-064x(17)30097-3.

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38

Cha, Shin, In Sang Chung, and Yong Rae Kwon. "Complexity measures for concurrent programs based on information-theoretic metrics." Information Processing Letters 46, no. 1 (1993): 43–50. http://dx.doi.org/10.1016/0020-0190(93)90195-f.

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39

Cho, S., R. Alamoudi, and S. Asfour. "Interaction-based complexity measure of manufacturing systems using information entropy." International Journal of Computer Integrated Manufacturing 22, no. 10 (2009): 909–22. http://dx.doi.org/10.1080/09511920902951393.

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40

Cao, Hai Wang, and Chao Gai Xue. "Self-Organization System Framework of Enterprise Information System Based on CAS." Advanced Materials Research 591-593 (November 2012): 2628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2628.

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In order to avoid enterprise information system (EIS) risk, the self-organization mechanism of EIS based on complex adaptive system (CAS) is studied. Firstly, self-organization properties of EIS are analyzed, which include open system, nonlinear characteristics, far from equilibrium and fluctuations. Secondly, the complex properties and complex adaptive properties of EIS self-organization are studied. The complex properties include multi-agent, active adaptation of agents, multi-level nature, technology complexity, organizational complexity, process complexity and environment complexity. The c

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41

Kang, Hyun-Seok, and Chi-Hyuck Jun. "Mutual information-based multi-output tree learning algorithm." Intelligent Data Analysis 25, no. 6 (2021): 1525–45. http://dx.doi.org/10.3233/ida-205367.

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A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning al

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42

Pei, Xiao Bing, and Shao Ping Lv. "Research on Effectiveness of Production Resource Allocation Based on Extended Information Entropy." Applied Mechanics and Materials 687-691 (November 2014): 5145–48. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.5145.

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The effective resource allocation in production system is the key to high performance. This paper firstly analyses the entropy increase which damages the factors’ operation, then the information entropy theory is extended to establish a complexity model based on size, difficulty, and state diversity. On the basis of complexity, the decline of factors’ utilization efficiency is described. Moreover, some specific management methods are introduced to illustrate the importance of complexity control.

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43

Mattos, Sérgio Henrique Vannucchi Leme de, Luiz Eduardo Vicente, Andrea Koga Vicente, Cláudio Bielenki Júnior, and José Roberto Castilho Piqueira. "Metrics based on information entropy applied to evaluate complexity of landscape patterns." PLOS ONE 17, no. 1 (2022): e0262680. http://dx.doi.org/10.1371/journal.pone.0262680.

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Landscape is an ecological category represented by a complex system formed by interactions between society and nature. Spatial patterns of different land uses present in a landscape reveal past and present processes responsible for its dynamics and organisation. Measuring the complexity of these patterns (in the sense of their spatial heterogeneity) allows us to evaluate the integrity and resilience of these complex environmental systems. Here, we show how landscape metrics based on information entropy can be applied to evaluate the complexity (in the sense of spatial heterogeneity) of patches

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44

Langer, Carlotta, and Nihat Ay. "Complexity as Causal Information Integration." Entropy 22, no. 10 (2020): 1107. http://dx.doi.org/10.3390/e22101107.

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Complexity measures in the context of the Integrated Information Theory of consciousness try to quantify the strength of the causal connections between different neurons. This is done by minimizing the KL-divergence between a full system and one without causal cross-connections. Various measures have been proposed and compared in this setting. We will discuss a class of information geometric measures that aim at assessing the intrinsic causal cross-influences in a system. One promising candidate of these measures, denoted by ΦCIS, is based on conditional independence statements and does satisf

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45

Abad, Andres G., and Jionghua Jin. "Complexity metrics for mixed model manufacturing systems based on information entropy." International Journal of Information and Decision Sciences 3, no. 4 (2011): 313. http://dx.doi.org/10.1504/ijids.2011.043025.

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46

Kamejima, Kohji. "Chromatic Information Adaptation for Complexity-Based Integration of Multi-Viewpoint Imagery." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2007 (May 5, 2007): 82–87. http://dx.doi.org/10.5687/sss.2007.82.

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47

"Information-Based Complexity." Science 243, no. 4895 (1989): 1142–43. http://dx.doi.org/10.1126/science.243.4895.1142-a.

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48

"Information-based complexity." Mathematics and Computers in Simulation 31, no. 1-2 (1989): 142. http://dx.doi.org/10.1016/0378-4754(89)90072-4.

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49

"2004 Information-Based Complexity Prize Committee." Journal of Complexity 20, no. 1 (2004): 4. http://dx.doi.org/10.1016/j.jco.2003.11.002.

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50

"2003 Information-Based Complexity Prize Committee." Journal of Complexity 18, no. 4 (2002): 911. http://dx.doi.org/10.1006/jcom.2002.0659.

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