Andrés Cano

775 total citations
33 papers, 404 citations indexed

About

Andrés Cano is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, Andrés Cano has authored 33 papers receiving a total of 404 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 14 papers in Signal Processing and 11 papers in Information Systems. Recurrent topics in Andrés Cano's work include Bayesian Modeling and Causal Inference (31 papers), Data Management and Algorithms (14 papers) and Data Mining Algorithms and Applications (11 papers). Andrés Cano is often cited by papers focused on Bayesian Modeling and Causal Inference (31 papers), Data Management and Algorithms (14 papers) and Data Mining Algorithms and Applications (11 papers). Andrés Cano collaborates with scholars based in Spain, Switzerland and Austria. Andrés Cano's co-authors include Serafı́n Moral, Antonio Salmerón, Luis M. de Campos, Javier G. Castellano, Joaquín Abellán, Thomas Lukasiewicz, Fábio Gagliardi Cozman, Juan M. Fernández‐Luna, Andrés R. Masegosa and Alessandro Antonucci and has published in prestigious journals such as Information Sciences, Knowledge-Based Systems and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).

In The Last Decade

Andrés Cano

31 papers receiving 359 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Andrés Cano Spain 12 331 120 91 81 46 33 404
Alessandro Antonucci Switzerland 12 308 0.9× 95 0.8× 79 0.9× 57 0.7× 18 0.4× 58 389
Jiří Vomlel Czechia 10 242 0.7× 53 0.4× 35 0.4× 35 0.4× 37 0.8× 30 335
Andrés R. Masegosa Spain 12 272 0.8× 91 0.8× 69 0.8× 18 0.2× 71 1.5× 24 383
Rosanna Verde Italy 11 213 0.6× 62 0.5× 51 0.6× 70 0.9× 34 0.7× 30 383
Meizhu Li China 11 129 0.4× 125 1.0× 73 0.8× 20 0.2× 29 0.6× 28 465
Adam Niewiadomski Poland 9 188 0.6× 67 0.6× 77 0.8× 38 0.5× 14 0.3× 35 265
David Harmanec United States 11 278 0.8× 208 1.7× 126 1.4× 18 0.2× 19 0.4× 19 405
Jochen Heinsohn Germany 5 177 0.5× 51 0.4× 43 0.5× 34 0.4× 39 0.8× 9 238
Urszula Chajewska United States 7 209 0.6× 112 0.9× 21 0.2× 41 0.5× 32 0.7× 13 331
Silvia Acid Spain 7 194 0.6× 56 0.5× 36 0.4× 31 0.4× 43 0.9× 9 266

Countries citing papers authored by Andrés Cano

Since Specialization
Citations

This map shows the geographic impact of Andrés Cano's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andrés Cano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrés Cano more than expected).

Fields of papers citing papers by Andrés Cano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrés Cano. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andrés Cano. The network helps show where Andrés Cano may publish in the future.

Co-authorship network of co-authors of Andrés Cano

This figure shows the co-authorship network connecting the top 25 collaborators of Andrés Cano. A scholar is included among the top collaborators of Andrés Cano based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrés Cano. Andrés Cano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cano, Andrés, et al.. (2022). Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models. Mathematics. 10(14). 2542–2542.
2.
Cano, Andrés, et al.. (2021). Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models. International Journal of Intelligent Systems. 36(11). 6913–6943. 1 indexed citations
3.
Moral, Serafı́n, et al.. (2021). Computation of Kullback–Leibler Divergence in Bayesian Networks. Entropy. 23(9). 1122–1122. 10 indexed citations
4.
Antonucci, Alessandro, et al.. (2016). Evaluating interval-valued influence diagrams. International Journal of Approximate Reasoning. 80. 393–411. 7 indexed citations
5.
Cano, Andrés, et al.. (2016). Using Binary Trees for the Evaluation of Influence Diagrams. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 24(1). 59–89. 4 indexed citations
6.
Cano, Andrés, et al.. (2015). Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams. International Journal of Approximate Reasoning. 70. 13–35. 2 indexed citations
7.
Cano, Andrés, et al.. (2012). Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks. International Journal of Approximate Reasoning. 54(4). 526–540. 11 indexed citations
8.
Cano, Andrés, et al.. (2012). Learning recursive probability trees from probabilistic potentials. International Journal of Approximate Reasoning. 53(9). 1367–1387. 7 indexed citations
9.
Cano, Andrés, et al.. (2011). A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 41(5). 1382–1394. 61 indexed citations
10.
Cano, Andrés, et al.. (2011). Learning with Bayesian networks and probability trees to approximate a joint distribution. 156. 624–629. 1 indexed citations
11.
Cano, Andrés, et al.. (2010). Approximate inference in Bayesian networks using binary probability trees. International Journal of Approximate Reasoning. 52(1). 49–62. 12 indexed citations
12.
Cano, Andrés, Fábio Gagliardi Cozman, & Thomas Lukasiewicz. (2006). Reasoning with imprecise probabilities. International Journal of Approximate Reasoning. 44(3). 197–199. 11 indexed citations
13.
Cano, Andrés, et al.. (2006). Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks. International Journal of Approximate Reasoning. 44(3). 261–280. 20 indexed citations
14.
Cano, Andrés, et al.. (2005). Application of a hill-climbing algorithm to exact and approximate inference in credal networks.. 88–97. 4 indexed citations
15.
Cano, Andrés, Serafı́n Moral, & Antonio Salmerón. (2003). Novel strategies to approximate probability trees in penniless propagation. International Journal of Intelligent Systems. 18(2). 193–203. 5 indexed citations
16.
Cano, Andrés & Serafı́n Moral. (2002). Using probability trees to compute marginals with imprecise probabilities. International Journal of Approximate Reasoning. 29(1). 1–46. 39 indexed citations
17.
Cano, Andrés, Juan M. Fernández‐Luna, & Serafı́n Moral. (2002). Computing probability intervals with simulated annealing and probability trees. Journal of Applied Non-Classical Logics. 12(2). 151–171. 8 indexed citations
18.
Cano, Andrés, Serafı́n Moral, & Antonio Salmerón. (2000). Penniless propagation in join trees. International Journal of Intelligent Systems. 15(11). 1027–1059. 1 indexed citations
19.
Salmerón, Antonio, Andrés Cano, & Serafı́n Moral. (2000). Importance sampling in Bayesian networks using probability trees. Computational Statistics & Data Analysis. 34(4). 387–413. 49 indexed citations
20.
Cano, Andrés & Serafı́n Moral. (1999). A Review of Propagation Algorithms for Imprecise Probabilities.. 51–60. 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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