965 resultados para Martínez, Angelita
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Duración (en horas): De 21 a 30 horas. Destinatario: Estudiante
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Este trabajo pretende ser de utilidad para cualquier, en general, persona que precise conocer las alternativas de financiación de las que dispone una empresa. En particular puede ser utilizado como material docente en asignaturas, tanto de la Licenciatura en Administración y Dirección de Empresas como de la Licenciatura en Economía, que aborden la financiación empresarial. El material se estructura en tres partes: •La primera parte, compuesta por un único capitulo se presenta la necesidad de conocer las distintas alternativas de financiación existentes y los criterios que se han de seguir para su elección: coste, vencimiento, propiedad y origen. •En la segunda parte, subdividida en cinco capítulos, se analizan cinco alternativas de financiación a corto plazo, presentando tanto sus características como el procedimiento para calcular su coste. •En la tercera parte, formada por dos capítulos, se estudian dos de las principales fuentes de financiación ajenas a largo plazo.
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416 p.
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481 p.
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324 p.
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475 p.
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184 p. : il.
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224 p.
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143 p.
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xix, 213 p.
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XX,560 p.
Estudio de la actividad aminopeptidásica en espermatozoides astenozoospérmicos : comparación clínica
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249 p.: il., col.
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186 p.
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The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.
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487 p. : il., col.