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Non-invasive risk assessment after myocardial infarction is a major but still unresolved goal in clinical cardiology. Various parameters such as ventricular late potentials, T-wave alternans, and repetitive ventricular extrasystoles have been shown to indicate an increased risk of sudden cardiac death. However, the practical use of these arrhythmic markers into clinical decision making remains difficult...
In this paper we discuss classifier architectures to categorize time series. Three different architectures for the fusion of local classifier decisions are presented and applied to classify recordings of cricket songs. Different features from local time windows are extracted automatically from the waveform of the sound patterns. These features are used to classify the whole time series. We present...
In this chapter we present a 3-D visual object recognition system for an autonomous mobile robot. This object recognition system performs the following three tasks: Object localization in the camera images, feature extraction, and classification of the extracted feature vectors with hierarchical radial basis function (RBF) networks.
Classifier selection aims to reduce the size of an ensemble of classifiers in order to improve its efficiency and classification accuracy. Recently an information-theoretic view was presented for feature selection. It derives a space of possible selection criteria and show that several feature selection criteria in the literature are points within this continuous space. The contribution of this paper...
Having a large game-tree complexity and being EXPTIME-complete, English Draughts, recently weakly solved during almost two decades, is still hard to learn for intelligent computer agents. In this paper we present a Temporal-Difference method that is nonlinear neural approximated by a 4-layer multi-layer perceptron. We have built multiple English Draughts playing agents, each starting with a randomly...
This contribution introduces a software framework enabling researchers to develop real-time pattern recognition and sensor fusion applications in an abstraction level above that of common programming languages in order to reduce and minimize programming errors and technical obstacles. Furthermore, a proof of concept using two separate instances of the process engine on different computers with audiovisual...
For many data mining applications, it is necessary to develop algorithms that use unlabeled data to improve the accuracy of the supervised learning. Co-Training is a popular semi-supervised learning algorithm. It assumes that each example is represented by two or more redundantly sufficient sets of features (views) and these views are independent given the class. However, these assumptions are not...
The goal of this work is to investigate the performance of emotion recognition using the three features of RASTA-PLP, loudness, and long term modulation features. Single classifiers utilizing only one and combinations of all three feature types are examined. The standard Berlin database of emotional speech is used to evaluate the performance of the proposed features, comprising recordings of seven...
We investigate the pattern completion performance of neural auto-associative memories composed of binary threshold neurons for sparsely coded binary memory patterns. By focusing on iterative retrieval, we are able to introduce effective threshold control strategies. These are investigated by means of computer simulation experiments and analytical treatment. To evaluate the systems performance we...
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