Titlebook: Intelligent Systems Design and Applications; 16th International C Ana Maria Madureira,Ajith Abraham,Paulo Novais Conference proceedings 201

终端

Innocence

Conference proceedings 2017lligent systems, intelligent technologies, and applications. The papers included address a wide variety of themes ranging from theories to applications of intelligent systems and computational intelligence area and provide a valuable resource for students and researchers in academia and industry alike.. .

博爱家

Agglomerative and Divisive Approaches to Unsupervised Learning in , Clusters,omerative (and three of its variants) and the other divisive, focusing on their performance in unsupervised learning tasks related to . clusters. Taking into account that the point sets considered are representative of gestalt clusters, the experiments show that the best results have been obtained when the agglomerative approach was used.

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Radial Basis Function Neural Networks for Datasets with Missing Values,rk, the RBFNN is modified to deal with missing data. For that, the expected squared distance approach is used to compute the RBF Kernel. The proposed approach showed promising results when compared to standard missing data strategies.

UTTER

Evaluation Method for an Adaptive Web Interface: GOMS Model,adaptive Web interface using a Bayesian networks approach. Then, a formal GOMS model approach was applied to the evaluation of our user interface for a specialized web application. The evaluation shows that the adaptive user interface was more comfortable than the fixed user interface.

insurgent

acquisition

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LATER

Agglomerative and Divisive Approaches to Unsupervised Learning in , Clusters,lated to the desired level of granularity the partition should have. The work described in this paper approaches two hierarchical algorithms, one agglomerative (and three of its variants) and the other divisive, focusing on their performance in unsupervised learning tasks related to . clusters. Taki

一大群

Improving Imputation Accuracy in Ordinal Data Using Classification,ferences between instances with and without missing data. This is a particular problem with ordinal data, where for example a sample of a population may have all failed to answer a specific question in a questionnaire. The existing methods such as listwise deletion, mean attribute substitution, and