By Ramón Martínez
Cuentos de suturaes un libro atípico y brillante
Una excepcional radiografía de lo más sórdido y florido de nuestros muchos síndromes. Escrito con el pulso firme de un cirujano y el sentido del humor de un anestesista, las siete historias que conforman este libro ahondan en lo peor de los angeles condición humana con extraordinaria mala leche y afiladísima pluma. Las soberbias ilustraciones de Judas Arrieta que acompañan a los relatos son obra de un artista de gran sensibilidad y poderosa pegada que ha sabido trasladar al papel con cariño y puntería el meollo de las historias.
Siete noticias, cada una tan desafiante como los angeles otra, para descubrir los aspetos más sombros del ser humano
Que el señor Somoza es un caballero de continente inalterable, exquisitos modales y ethical intachable es evidente; y que en el carácter del señor Riesco destacan su afable semblante, su humor astuto y su discreta prudencia no lo duda nadie. Pero ahora, el señor Somoza, en un rapto alucinado y sin venir a cuento, le acaba de meter un dedo en el ojo al señor Riesco, que está sentado a su lado. Éste se ha sorprendido bastante, por lo inopinado de l. a. agresión, supongo, se ha llevado los angeles servilleta a los angeles cara y se ha ido directamente al cuarto de baño. El señor Somoza se ha soplado las puntas de los dedos y los ha frotado delicadamente contra l. a. solapa.
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Extra info for Cuentos de sutura: Noticias
It is worth pointing out that results reported in this section captured all the experiments that have been carried out. However, due to space limitation, the results of each scenario are summarized in a separate table, followed by a detailed table of winning solutions that represent the signiﬁcant diﬀerence in accuracy that has been achieved frequently. In the ﬁrst set of experiments, we used the ﬁrst variation given by Eqs. 8 and 9 above. Here we chose ???? = ???? = 1, therefore, only 1 tree may be added/removed in each iteration.
Similar to Table 2, Table 6 compares the performance of DRF with itself before and after applying RD. This represents the typical implementation of the RD, where the subforest with a considerable over average accuracy can grow with more than one tree in each iteration. Table 7 shows the accuracy of the winning datasets. As demonstrated in the previously discussed experiments, the increase in accuracy is signiﬁcant in some of the datasets, suggesting the eﬀectiveness of RD in the feature engineering process.
DRF is built using random subspacing with each subspace used to construct a subforest. DRF was used to impose diversity in Random Forests. Noting that subspaces may vary in their discriminative power, we applied RD to perform collective feature engineering, allowing stronger subspaces to grow and weaker ones to shrink, and potentially disappear. As was demonstrated in Tables 2, 4, 6, and 8, favourable results were obtained demonstrating the potential of the proposed method. In our experiments, we have A Replicator Dynamics Approach to Collective Feature .
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