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The management of the knowledge is considered as a leading managerial approaches and most followed. Due to its ability to provide information and knowledge, particularly those in minds of individuals and that will improve the image of the organization and raise the level of their performances . We find the regulatory environment of libr aries and especially the university ones are the Best environment to activate this concept. And to be most need , the consultative meetings are the most important elements that helps to activate the Knowledge management within the university libraries as a violin best suited for the exchange of knowledge and ideas and relay experiences leading up to the decision - making collective effective . In this sense this study addressed the subject of one of the particles of knowledge management raised frequently in recent years and the most important conclusions of the study are: That the consultative meetings a prominent role in the activation of knowledge management in the Central Library of the University of Larbi Ben M'hidi , Oum - El - Bouaghi - Algeria . as it contributes to all processes activated, and is working to achieve a number of these goals . Despite held to a moderate and not granted the right to adequate standard library ; therefore the more sought university libraries Algerian JavaScript knowledge m anagement formally and clear , and provided the requirements and the basic mechanisms to activate , which is consultative meetings - as I have already reached - the most important of the more contributed to the advancement and to right level of the global university libraries [ABSTRACT FROM AUTHOR]

In this paper, we perform an empirical comparison of the classification error of several ensemble methods based on classification trees. This comparison is performed by using 14 data sets that are publicly available and that were used by Lim, Loh and Shih [Lim, T., Loh, W. and Shih, Y.-S., 2000, A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning , 40, 203–228.]. The methods considered are a single tree, Bagging, Boosting (Arcing) and random forests (RF). They are compared from different perspectives. More precisely, we look at the effects of noise and of allowing linear combinations in the construction of the trees, the differences between some splitting criteria and, specifically for RF, the effect of the number of variables from which to choose the best split at each given node. Moreover, we compare our results with those obtained by Lim et al . [Lim, T., Loh, W. and Shih, Y.-S., 2000, A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning , 40, 203–228.]. In this study, the best overall results are obtained with RF. In particular, RF are the most robust against noise. The effect of allowing linear combinations and the differences between splitting criteria are small on average, but can be substantial for some data sets. [ABSTRACT FROM AUTHOR]