Consensus clustering algorithms are used to improve properties of traditional clustering methods, especially their accuracy and robustness. In this article, we introduce our approach that is based on a refinement of the set of initial partitions and uses differential evolution algorithm in order to find the most valid solution. Properties of the algorithm are demonstrated on four benchmark datasets.
In contrary to general interpretations of opera buffa, the presence and importance of arias and ensembles based primarily on emotions (and not only action) are crucial for the genre’s dramaturgy as well as for its historical development. The presence of lyrical arias in opera buffa has its origins in the traditional comic dramaturgy (one or more couples of serious lovers), the number, form and functions of such arias, however, changed considerably during the 18th century. Not only the use of Tuscan Italian, but also adopting new music features of opera seria for lyrical arias of noble lovers (in 30ties) led to the rapid dissemination of the genre. Similarly, broadening of the typology of characters and its emotions in the works of Goldoni and his composers, mostly the including of the sentimental plots and its new kind of heroine, supported the popularity of opera buffa and its transformation to the leading operatic genre in the second half of 18th century., Marc Niubo., and Obsahuje bibliografické odkazy
Extreme learning machine (ELM) is an emergent method for training single hidden layer feedforward neural networks (SLFNs) with extremely fast training speed, easy implementation and good generalization performance. This work presents effective ensemble procedures for combining ELMs by exploiting diversity. A large number of ELMs are initially trained in three different scenarios: the original feature input space, the obtained feature subset by forward selection and different random subsets of features. The best combination of ELMs is constructed according to an exact ranking of the trained models and the useless networks are discarded. The experimental results on several regression problems show that robust ensemble approaches that exploit diversity can effectively improve the performance compared with the standard ELM algorithm and other recent ELM extensions.