Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Accuracy Tuning on Combinatorial Neural Model
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Optimizations of the Combinatorial Neural Model
SBRN '98 Proceedings of the Vth Brazilian Symposium on Neural Networks
Learning in the combinatorial neural model
IEEE Transactions on Neural Networks
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In this paper we apply the Combinatorial Neural Model (CNM) to help scientists in Applied Social Sciences to understand a proactive agent in relation to the social environment. CNM is a hybrid neural network that represents an alternative to overcome the black box limitation of the Multilayer Perceptron by applying the neural network structure along with a symbolic processing. The model built comprises the recognition of patterns from a socioeconomic survey and from a set of written texts, and aims at understanding the student point of view about its role in the society. The self-perception of the young in relation to willingness for social proactivity was studied. Proactivity was taken as the possibility of transforming society from the perspective of social inequality. The whole process was driven under CRISP-DM guidelines. The model succeeded in identifying different rules that characterize non-proactive students. Results show that current approach is useful for subsidizing educators and managers of educational institutions in decision making with information on students' profile.