M. Julia Flores

发表

Initial breeding value prediction on Manchego sheep by using rule-based systems

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2007, Expert Syst. Appl..

Incremental Compilation of Bayesian Networks Based on Maximal Prime Subgraphs

José A. Gámez, Kristian G. Olesen, M. Julia Flores, 2011, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Meta-prediction of semi-naive Bayesian network classifiers based on dataset complexity characterization

M. Julia Flores, José A. Gámez, 2012 .

GAODE and HAODE: two proposals based on AODE to deal with continuous variables

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2009, ICML '09.

Integration of contextual information into the scene classification problem

Jose Miguel Puerta, Jesus Martínez-Gómez, M. Julia Flores, 2017, Robotics Auton. Syst..

Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2010, IEA/AIE.

Breeding Value Classification in Manchego Sheep: A Study of Attribute Selection and Construction

José A. Gámez, M. Julia Flores, 2005, KES.

Use of Explanation Treesto Describe the State Space of a Probabilistic-Based Abduction Problem

José A. Gámez, Serafín Moral, M. Julia Flores, 2008, Innovations in Bayesian Networks.

Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter?

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2011, Applied Intelligence.

Applicability of Estimation of Distribution Algorithms to the Fuzzy Rule Learning Problem: A Preliminary Study

José A. Gámez, M. Julia Flores, 2002, ICEIS.

Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers

José A. Gámez, Antonio Salmerón, Ana M. Martínez, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

Combining state and transition models with dynamic Bayesian networks

Ann E. Nicholson, M. Julia Flores, A. Nicholson, 2011 .

Prediction of coffee rust disease using Bayesian networks

Ann E. Nicholson, M. Julia Flores, Cora B. Pérez-Ariza, 2012, PGM 2012.

Comparison between Bayesian network classifiers and SVMs for semantic localization

Jose Miguel Puerta, Jesus Martínez-Gómez, M. Julia Flores, 2016, Expert Syst. Appl..

Incorporating expert knowledge when learning Bayesian network structure: A medical case study

Kevin B. Korb, Ann E. Nicholson, M. Julia Flores, 2011, Artif. Intell. Medicine.

The PDG-Mixture Model for Clustering

José A. Gámez, Jens Dalgaard Nielsen, M. Julia Flores, 2009, DaWaK.

Supervised Classification with Bayesian Networks: A Review on Models and Applications

Ana M. Martínez, M. Julia Flores, José A. Gámez, 2012 .

Seleccion genetica para la mejora de la raza ovina manchega mediante tecnicas de Mineria de Datos

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2006, Inteligencia Artif..

Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology

José M. Puerta, M. Julia Flores, José A. Gámez, 2006, Towards a New Evolutionary Computation.

Domains of competence of the semi-naive Bayesian network classifiers

José A. Gámez, Ana M. Martínez, M. Julia Flores, 2014, Inf. Sci..

Evaluation of a Thermal-Comfort Control System Using Real Data

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2012, KES.

Applications of hybrid dynamic Bayesian networks to water reservoir management

Rosa F. Ropero, Rafael Rumí, M. Julia Flores, 2017 .

The Independency tree model: a new approach for clustering and factorisation

José A. Gámez, Serafín Moral, M. Julia Flores, 2006, Probabilistic Graphical Models.

Dynamic Bayesian Networks for semantic localization in robotics

Ann E. Nicholson, Fernando Rubio, M. Julia Flores, 2014 .

Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers

José A. Gámez, Geoffrey I. Webb, Ana M. Martínez, 2012, HAIS.

Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space

José A. Gámez, M. Julia Flores, Serafín Moral, 2005, ECSQARU.

HODE: Hidden One-Dependence Estimator

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2009, ECSQARU.

Incremental compilation of Bayesian networks

José A. Gámez, Kristian G. Olesen, M. Julia Flores, 2002, UAI.

Machine learning for music genre: multifaceted review and experimentation with audioset

M. Julia Flores, Jaime Ramírez, Jaime Ramírez, 2019, Journal of Intelligent Information Systems.

Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets

José A. Gámez, M. Julia Flores, 2015, ICAART.

Triangulation of Bayesian networks by retriangulation

José A. Gámez, M. Julia Flores, 2003, Int. J. Intell. Syst..

Drawing a baseline in aesthetic quality assessment

M. Julia Flores, Fernando Rubio, Jose M. Puerta, 2018, International Conference on Machine Vision.

Plug & Play Object Oriented Bayesian Networks

Finn Verner Jensen, M. Julia Flores, Olav Bangsø, 2003, CAEPIA.

Mining the ESROM: A study of breeding value classification in Manchego sheep by means of attribute selection and construction

Juan L. Mateo, M. Julia Flores, José A. Gámez, 2008 .

A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks

José A. Gámez, M. Julia Flores, M. Flores, 2007 .