Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Resilience by solidary ties

This article considers an empirical approach to the relationships among three well known concepts: “Benevolence” (Schwartz), Solidarity and Resilience ("Subjective wellbeing scale" - SWB). The first concept refers to cultural values, the second one to social networks and the third to the ability to recover from crisis. The measurement of solidarity has been done from the point of view of supportive ties. The baseline hypothesis considers that the presence of a high value in Benevolence contributes to the involvements in solidarity networks. Participation in supportive relationships facilitates recovery from personal crisis. Using data from the European Social Survey (ESS6), we conclude from this structural analysis that the resilience reflected in a society is partly a consequence of the supportive networks shaped by the presence of benevolence values.