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Ana M. Martínez
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2020 – today
- 2020
- [j10]Andrés R. Masegosa, Ana M. Martínez
, Darío Ramos-López
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24(3): 665-688 (2020)
2010 – 2019
- 2019
- [j9]Andrés R. Masegosa, Ana M. Martínez
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163: 595-597 (2019) - 2017
- [j8]Andrés R. Masegosa
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88: 435-451 (2017) - [j7]Shenglei Chen
Selective AnDE for large data learning: a low-bias memory constrained approach. Knowl. Inf. Syst. 50(2): 475-503 (2017) - [j6]Darío Ramos-López
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6(2): 133-144 (2017) - [j5]Shenglei Chen, Ana M. Martínez
Sample-Based Attribute Selective An DE for Large Data. IEEE Trans. Knowl. Data Eng. 29(1): 172-185 (2017) - [i2]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR abs/1704.01427 (2017) - 2016
- [j4]Andrés R. Masegosa, Ana M. Martínez
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8. IEEE Comput. Intell. Mag. 11(2): 41-54 (2016) - [j3]Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar Abbas Zaidi:
Scalable Learning of Bayesian Network Classifiers. J. Mach. Learn. Res. 17: 44:1-44:35 (2016) - [c14]Antonio Salmerón, Anders L. Madsen
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI 2016: 743-750 - [c13]Rafael Cabañas
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops 2016: 1284-1287 - [c12]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models 2016: 321-332 - [c11]Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen:
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models 2016: 415-425 - [i1]Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani:
Probabilistic Graphical Models on Multi-Core CPUs using Java 8. CoRR abs/1604.07990 (2016) - 2015
- [c10]Antonio Salmerón, Darío Ramos-López
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA 2015: 36-46 - [c9]Hanen Borchani
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA 2015: 72-83 - [c8]Hanen Borchani
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI 2015: 17-26 - 2014
- [j2]M. Julia Flores
Domains of competence of the semi-naive Bayesian network classifiers. Inf. Sci. 260: 120-148 (2014) - [c7]Shenglei Chen, Ana M. Martínez
Highly Scalable Attribute Selection for Averaged One-Dependence Estimators. PAKDD (2) 2014: 86-97 - 2012
- [c6]Ana M. Martínez
Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers. HAIS (2) 2012: 151-162 - 2011
- [j1]M. Julia Flores
Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter? Appl. Intell. 34(3): 372-385 (2011) - [c5]M. Julia Flores
Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. ISDA 2011: 593-598 - 2010
- [c4]Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth:
Towards a More Expressive Model for Dynamic Classification. FLAIRS 2010 - [c3]M. Julia Flores
Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers. IEA/AIE (1) 2010: 570-579
2000 – 2009
- 2009
- [c2]M. Julia Flores
HODE: Hidden One-Dependence Estimator. ECSQARU 2009: 481-492 - [c1]M. Julia Flores, José A. Gámez
GAODE and HAODE: two proposals based on AODE to deal with continuous variables. ICML 2009: 313-320
Coauthor Index
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