Publicaciones en las que colabora con JUAN JOSE DEL COZ VELASCO (24)

2020

  1. Improving the ϵ -approximate algorithm for Probabilistic Classifier Chains

    Knowledge and Information Systems, Vol. 62, Núm. 7, pp. 2709-2738

2019

  1. Dynamic ensemble selection for quantification tasks

    Information Fusion, Vol. 45, pp. 1-15

2017

  1. A family of admissible heuristics for A* to perform inference in probabilistic classifier chains

    Machine Learning, Vol. 106, Núm. 1, pp. 143-169

  2. A heuristic in A* for inference in nonlinear Probabilistic Classifier Chains

    Knowledge-Based Systems, Vol. 126, pp. 78-90

  3. Using ensembles for problems with characterizable changes in data distribution: A case study on quantification

    Information Fusion, Vol. 34, pp. 87-100

2016

  1. An overview of inference methods in probabilistic classifier chains for multilabel classification

    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 6, Núm. 6, pp. 215-230

2015

  1. Using A∗ for inference in probabilistic classifier chains

    IJCAI International Joint Conference on Artificial Intelligence

2014

  1. Dependent binary relevance models for multi-label classification

    Pattern Recognition

2013

  1. Predicting fertility from seminal traits: Performance of several parametric and non-parametric procedures

    Livestock Science, Vol. 155, Núm. 1, pp. 137-147

2011

  1. Aggregating independent and dependent models to learn multi-label classifiers

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2010

  1. Adapting decision DAGs for multipartite ranking

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Explaining the genetic basis of complex quantitative traits through prediction models

    Journal of Computational Biology, Vol. 17, Núm. 12, pp. 1711-1723

2009

  1. Prediction and inheritance of phenotypes

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2007

  1. How to learn consumer preferences from the analysis of sensory data by means of support vector machines (SVM)

    Trends in Food Science and Technology, Vol. 18, Núm. 1, pp. 20-28

2004

  1. Analyzing sensory data using non-linear preference learning with feature subset selection

    Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

  2. Discovering relevancies in very difficult regression problems: Applications to sensory data analysis

    Frontiers in Artificial Intelligence and Applications

  3. Feature subset selection for learning preferences: A case study

    Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

2003

  1. Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses

    Meat Science, Vol. 64, Núm. 3, pp. 249-258

2002

  1. Técnicas de Inteligencia Artificial en la clasificación de canales bovinas

    Nuestra cabaña, Núm. 316, pp. 12-18

2001

  1. The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

    Trends in Food Science and Technology, Vol. 12, Núm. 10, pp. 370-381