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The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
Recognition of social styles of people is an interesting but relatively unexplored task. Recognizing "style" appears to be a quite different problem than categorization, it is like recognizing a letter's font as opposed to recognizing the letter itself. Similar-looking things must be mapped to different categories. Hence a priori it would appear that features that are good for categorization...
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based...
The neural networks are well known as that they have an ability of approximation of any nonlinear function, and they are applied for data prediction in many fields. The parameters of neural networks, the thresholds and the weights between nodes, are updated by using given data. The performance of a neural network, for example prediction accuracy, is evaluated by the degree of the amount of the prediction...
In recent years, progress in the field of artificial neural networks provides a very important tool for complex problems in pattern recognition, data mining and medical diagnosis. The training algorithms of neural networks play an important role for adjustment the network parameters. Different algorithms have been presented for training neural networks; the most common one is the use of gradient descent...
In this paper, we present a model for Turkish speech recognition. The model is syllable-based, where the recognition is performed through syllables as speech recognition units. The main goal of the model is to recognize as much as possible of a given continuous speech by identifying only a small set of syllables in the language. For that purpose, only the syllable types with a higher frequency are...
Effective image classification becomes an important issue in content-based image retrieval since it can help to organize the massive amount of digital images and serve for many applications such as object identification, web people search, etc. In this paper, the image classification problem is considered as a Multiple-Instance Learning problem, and Multiple-Instance Decision-Based Neural Networks...
Despite early success in automatic chord recognition, recent efforts are yielding diminishing returns while basically iterating over the same fundamental approach. Here, we abandon typical conventions and adopt a different perspective of the problem, where several seconds of pitch spectra are classified directly by a convolutional neural network. Using labeled data to train the system in a supervised...
The paper presents a classification of the protein surface atom neighbourhoods from the hydrophobicity perspective. Hydrophobicity is the property which is considered around each surface atom. The actual hydrophobicity distribution on the atoms that form an atom's vicinity is replaced by an equivalent hydrophobicity density distribution, computed in a standardized octagonal pattern around the atom...
For purpose of detecting cardiovascular diseases (CVDs) hierarchically via hemodynamic parameters (HDPs) derived from sphygmogram, a fused hierarchical neural networks (FHNNs) scheme is proposed, which provides a noninvasive way to detect CVDs. To deduce conclusion via FHNNs, method of variance analysis is used to categorize HDPs. The categorized HDP sets are then inhaled by different sub neural networks...
In this investigation, a cancer classification approach is presented using clustering based gene selection and artificial neural networks. To address the so called ‘curse of dimensionality’ a T-statistic feature selection method, one of the univariate filter techniques, is used to select the most informative genes. However, instead of selecting a small group of relevant genes at once from the whole...
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