By W. Somerset Maugham

This ultimate vintage number of tales finds Somerset Maugham's distinctive expertise for exposing and exploring the sour realities of human relationships. amazing stories of affection, infidelity, ardour and prejudice, the tales variety from 'The Lotus Eater' within which a guy has a imaginative and prescient of a lifetime of bliss within the Mediterranean, to the astringent stories of 'The Outstation' and 'The again of Beyond' in Malaya and South East Asia. mostly set in favorite Maugham kingdom, this vibrant assortment brilliantly inspires the numbered days of the British Empire.

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Collected Short Stories Volume 4

This ultimate vintage number of tales unearths Somerset Maugham's specified expertise for exposing and exploring the sour realities of human relationships. magnificent stories of affection, infidelity, ardour and prejudice, the tales variety from 'The Lotus Eater' during which a guy has a imaginative and prescient of a lifetime of bliss within the Mediterranean, to the astringent stories of 'The Outstation' and 'The again of Beyond' in Malaya and South East Asia.

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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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Collected Short Stories Volume 4 by W. Somerset Maugham
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