We use a clustering signature, based on a recently introduced generalization of the clustering coefficient to directed networks, to analyze 16 directed real-world networks of five different types: social networks, genetic transcription networks, word adjacency networks, food webs, and electric circuits. We show that these five classes of networks are cleanly separated in the space of clustering...

This article demonstrates a new conceptor network based classifier in classifying images. Mathematical descriptions and analysis are presented. Various tests are experimented in this context using three benchmark datasets: MNIST, CIFAR10 and CIFAR-100. The experiments displayed that conceptor network can offer superior results and flexible configurations than conventional classifiers such as So...

In this paper we study supervised and semi-supervised classification of e-mails. We consider two tasks: filing e-mails into folders and spam e-mail filtering. Firstly, in a supervised learning setting, we investigate the use of random forest for automatic e-mail filing into folders and spam e-mail filtering. We show that random forest is a good choice for these tasks as it runs fast on large an...

Learning how to classify sensor data is one of the basic learning tasks in engineering. Data from sensors are usually made available over time and are classiied according to the behavior they exhibit in speciic time intervals. I address the problem of classifying nite, univariate, time series that are governed by unknown deterministic processes contaminated by noise. Time series in the same cla...

While the technique of active learning has been applied successfully in improving text classification, its use in email classification has still not been explored. This paper examines several of the stateof-the-art algorithms for active learning with support vector machines as they are applied to email folder classification. We also introduce several extensions to these methods specifically des...

EEG signals contain information directly related to cognitive activity. This paper presents a method classify the images person imagines via provided by signals. The relating objects ‘tree’, ‘house’, ‘plane’ and ‘dog’ have been reconstructed. We used convolutional neural networks obtain reconstruction of genetic algorithm find parameters this network. results obtained evaluated means Chebychev ...