José D. Martín-Guerrero

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BACKGROUND This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and(More)
Clustering techniques and classification trees are two of the main techniques used in data mining but, at present, there is still a lack of visualization methods for these tools. Many graphs associated with clustering, also with hierarchical clustering, do not give any information about the values of the centroids’ attributes and the relationships among(More)
The comfort in footwear is essential because the foot is one of the structures of the human body that supports more weight. Moreover, consumers are demanding ever higher and higher levels of comfort and functionality in shoes. Hence, the analysis of the comfort in the footwear industry is of great interest. This paper proposes the use of SOMs to(More)
In this paper, we propose the use of the Adaptive Resonance Theory, and more specifically the ART2 neural network for carrying out a clustering of web users; this is because of its capabilities to find clusters regardless of whether the clusters present a certain size or shape. Moreover, this algorithm does not need to know the number of clusters in(More)
This paper proposes some useful modifications to the Expanded Range Approximation (ERA) learning algorithm. In channel equalisation, it is a common practise to recover the original signal using an artificial neural network. The ERA algorithm is an alternative to the usual backpropagation algorithm that mitigates the effect of local minima during the(More)
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