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This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval.(More)
Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the(More)
The new SeDeM Method is proposed for testing the batch-to-batch reproducibility of the same active pharmaceutical ingredient (API) in powder form. The procedure describes the study of the galenic properties of substances in powder form in terms of the applicability of direct compression technology. Through experimental determination of the SeDeM Method(More)
Application of the new SeDeM Method is proposed for the study of the galenic properties of excipients in terms of the applicability of direct-compression technology. Through experimental studies of the parameters of the SeDeM Method and their subsequent mathematical treatment and graphical expression (SeDeM Diagram), six different DC diluents were analysed(More)
In this paper we give a formal definition of the high-order Boltzmann machine (BM), and extend the well-known results on the convergence of the learning algorithm of the two-order BM. From the Bahadur-Lazarsfeld expansion we characterize the probability distribution learned by the high order BM. Likewise a criterion is given to establish the topology of the(More)
Mel frequency cepstral coefficients (MFCCs) are a standard tool for automatic speech recognition (ASR), but they fail to capture part of the dynamics of speech. The nonlinear nature of speech suggests that extra information provided by some nonlinear features could be especially useful when training data are scarce or when the ASR task is very complex. In(More)