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- Haydemar Núñez, Cecilio Angulo, Andreu Català
- IWANN
- 2003

An hybrid SVM-symbolic architecture for classification tasks is proposed in this work. The learning system relies on a support vector machine (SVM), meanwhile a rule extraction module translate the embedded knowledge in the trained SVM in the form of symbolic rules. The new representation is useful to understand the nature of the problem and its solution.… (More)

- Haydemar Núñez, Cecilio Angulo, Andreu Català
- Rule Extraction from Support Vector Machines
- 2008

- Luis González Abril, Haydemar Núñez, Cecilio Angulo, Francisco Velasco Morente
- Appl. Soft Comput.
- 2014

- Haydemar Núñez, Cecilio Angulo, Andreu Català
- Neural Processing Letters
- 2006

In this article, we propose some methods for deriving symbolic interpretation of data in the form of rule based learning systems by using Support Vector Machines (SVM). First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for… (More)

- Haydemar Núñez, Luis González Abril, Cecilio Angulo
- ESANN
- 2011

Standard learning algorithms may perform poorly when learning from unbalanced datasets. Based on the Fisher’s discriminant analysis, a post-processing strategy is introduced to deal datasets with significant imbalance in the data distribution. A new bias is defined, which reduces skew towards the minority class. Empirical results from experiments for a… (More)

- Haydemar Núñez, Cecilio Angulo, Andreu Català
- SBRN
- 2002

- Haydemar Núñez, Cecilio Angulo, Andreu Català
- Studies in Computational Intelligence
- 2002

Support vector machines (SVMs) are learning systems based on the statistical learning theory, which are exhibiting good generalization ability on real data sets. Nevertheless, a possible limitation of SVM is that they generate black box models. In this work, a procedure for rule extraction from support vector machines is proposed: the SVM+Prototypes method.… (More)

Two methods for the symbolic interpretation of both, Support Vector Machines (SVM) and Radial Basis Function Neural Networks (RBFNN) are proposed. These schemes, based on the combination of support vectors and prototype vectors by means of geometry produce rules in the form of ellipsoids and hyper-rectangles. Results obtained from a certain number of… (More)

- Haydemar Núñez, Cecilio Angulo, Andreu Català
- IBERAMIA
- 2002

In this work, a procedure for rule extraction from radial basis function (RBFN) networks is proposed. The algorithm is based on the use of a support vector machine (SVM) as a frontier pattern selector. By using geometric methods, centers of the RBF units are combined with support vectors in order to construct regions (ellipsoids or hyper-rectangles) in the… (More)

- Haydemar Núñez, Esmeralda Ramos
- CLEI
- 2012