Ana M. Martínez

Publication Activity (10 Years)

Years Active: 2009-2020
Publications (10 Years): 17

Top Topics

Probabilistic Graphical Models

Attribute Selection

Top Venues

Probabilistic Graphical Models

Int. J. Approx. Reason.

Publications

Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24 (3) (2020)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163 (2019)
Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb, Limin Wang
DE for Large Data. IEEE Trans. Knowl. Data Eng. 29 (1) (2017)
Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb, Limin Wang
Selective AnDE for large data learning: a low-bias memory constrained approach. Knowl. Inf. Syst. 50 (2) (2017)
Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6 (2) (2017)
Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88 (2017)
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 (2017)
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)
Ana M. Martínez, Geoffrey I. Webb, Shenglei Chen, Nayyar Abbas Zaidi
Scalable Learning of Bayesian Network Classifiers. J. Mach. Learn. Res. 17 (2016)
Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI (2016)
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)
Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops (2016)
Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani
Probabilistic Graphical Models on Multi-Core CPUs using Java 8. CoRR (2016)
Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8. IEEE Comput. Intell. Mag. 11 (2) (2016)
Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA (2015)
Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA (2015)
Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI (2015)
M. Julia Flores, José A. Gámez, Ana M. Martínez
Domains of competence of the semi-naive Bayesian network classifiers. Inf. Sci. 260 (2014)
Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb
Highly Scalable Attribute Selection for Averaged One-Dependence Estimators. PAKDD (2) (2014)
Ana M. Martínez, Geoffrey I. Webb, M. Julia Flores, José A. Gámez
Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers. HAIS (2) (2012)
M. Julia Flores, José A. Gámez, Ana M. Martínez, Antonio Salmerón
Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. ISDA (2011)
M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta
Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter? Appl. Intell. 34 (3) (2011)
Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth
Towards a More Expressive Model for Dynamic Classification. FLAIRS Conference (2010)
M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta
Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers. IEA/AIE (1) (2010)
M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta
HODE: Hidden One-Dependence Estimator. ECSQARU (2009)
M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta
GAODE and HAODE: two proposals based on AODE to deal with continuous variables. ICML (2009)