971 resultados para Super Bowl
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Autor tomado de la h. 2
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Hay un ejemplar encuadernado con: Quarta pars abulensis Super Mattheum a duodecimo usqz ad decimumseptimum capitulum inclusive (XVI/198).
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Texto fechado en Valencia, 25 de septiembre 1741
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Inicial grab. xil
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Inicial grab. xil
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En el verso de A3 grab. xil
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Pie de imp. to mado de colofón
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Sign.: A-O8, P2
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Copia digital: Biblioteca valenciana, 2010
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Copia digital: Biblioteca valenciana, 2010
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Texto paralelo latín-español
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The sparse differential resultant dres(P) of an overdetermined system P of generic nonhomogeneous ordinary differential polynomials, was formally defined recently by Li, Gao and Yuan (2011). In this note, a differential resultant formula dfres(P) is defined and proved to be nonzero for linear "super essential" systems. In the linear case, dres(P) is proved to be equal, up to a nonzero constant, to dfres(P*) for the supper essential subsystem P* of P.
Constitutio super Congregationibus Generalibus Clericorum Regularium Societatis Iesu [Texto impreso]
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Precede al tít. : "Sanctissimi in Christo Patris et Domini Nostri Domini Benedicti Diuina Prouidentia Papae XIV"
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The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.