11 research outputs found

    Uticaj dodavanja zeolita na kvalitet mesa pastrmke

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    U radu je ispitivan uticaj zeolita tipa "Minazel" kao aditiva hrane za pastrmke primenjenog u koncentraciji od 1% i 2% na sledeće proizvodne parametre: osnovni hemijski sastav mesa, mikotoksikološku ispravnost, senzorne osobine, randman i koncentraciju pojedinih makro i mikroelemenata. Primenjene koncentracije zeolita, kao aditiva hrane nisu uticale na hemijski sastav pastrmskog mesa, koncentraciju minerala (Ca, Cu, Zn, Pb i Mn) i njegove senzorne osobine. Izvesni stepen pozitivnog uticaja ispoljen je u pogledu randmana mesa i prosečne telesne mase pastrmki posle ezenteracije

    The Distributed Boosting Algorithm

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    In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneous databases that cannot be merged at a single location. Our distributed boosting algorithm can also be used as a parallel classification technique, where a massive database that cannot fit into main computer memory is partitioned into disjoint subsets for a more efficient analysis. In the proposed method, at each boosting round the classifiers are first learned from disjoint datasets and then exchanged amongst the sites. Finally the classifiers are combined into a weighted voting ensemble on each disjoint data set. The ensemble that is applied to an unseen test set represents an ensemble of ensembles built on all distributed sites. In experiments performed on four large data sets the proposed distributed boosting method achieved classification accuracy comparable or even slightly better than the standard boosting algorithm while requiring less memory and less computational time. In addition, the communication overhead of the distributed boosting algorithm is very small making it a viable alternative to the standard boosting for large-scale databases
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