Ana M. Martínez

Orcid: 0000-0002-4220-8358

According to our database1, Ana M. Martínez authored at least 24 papers between 2009 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2020
Analyzing concept drift: A case study in the financial sector.
Intell. Data Anal., 2020

2019
AMIDST: A Java toolbox for scalable probabilistic machine learning.
Knowl. Based Syst., 2019

2017
Sample-Based Attribute Selective A<i>n</i> DE for Large Data.
IEEE Trans. Knowl. Data Eng., 2017

MAP inference in dynamic hybrid Bayesian networks.
Prog. Artif. Intell., 2017

Selective AnDE for large data learning: a low-bias memory constrained approach.
Knowl. Inf. Syst., 2017

Scaling up Bayesian variational inference using distributed computing clusters.
Int. J. Approx. Reason., 2017

2016
Scalable Learning of Bayesian Network Classifiers.
J. Mach. Learn. Res., 2016

Probabilistic Graphical Models on Multi-Core CPUs Using Java 8.
IEEE Comput. Intell. Mag., 2016

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

d-VMP: Distributed Variational Message Passing.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Financial Data Analysis with PGMs Using AMIDST.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs.
Proceedings of the ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, 2016

2015
Dynamic Bayesian modeling for risk prediction in credit operations.
Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence, 2015

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach.
Proceedings of the Advances in Intelligent Data Analysis XIV, 2015

Parallel Importance Sampling in Conditional Linear Gaussian Networks.
Proceedings of the Advances in Artificial Intelligence, 2015

2014
Domains of competence of the semi-naive Bayesian network classifiers.
Inf. Sci., 2014

Highly Scalable Attribute Selection for Averaged One-Dependence Estimators.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

2012
Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers.
Proceedings of the Hybrid Artificial Intelligent Systems - 7th International Conference, 2012

2011
Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter?
Appl. Intell., 2011

Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers.
Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

2010
Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers.
Proceedings of the Trends in Applied Intelligent Systems, 2010

Towards a More Expressive Model for Dynamic Classification.
Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, 2010

2009
GAODE and HAODE: two proposals based on AODE to deal with continuous variables.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

HODE: Hidden One-Dependence Estimator.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009


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