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Decision trees are attractive classifiers due to their high execution speed. But trees derived with traditional methods often cannot be grown to arbitrary complexity for possible loss of generalization accuracy on unseen data. The limitation on complexity usually means suboptimal accuracy on training data. Following the principles of stochastic modeling, we propose a method to construct tree-based classifiers whose capacity can be arbitrarily expanded for increases in accuracy for both training and unseen data. The essence of the method is to build multiple trees in randomly selected subspaces of the feature space. Trees in different subspaces generalize their classification in complementary ways, and their combined classification can be monotonically improved. The validity of the method is demonstrated through experiments on the recognition of handwritten digits.

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Translation: A Political Act

Román Álvarez, Ma Carmen África Vidal

academia.edu·1996

The paper explores the evolution of Translation Studies alongside societal changes, emphasizing the political dimensions inherent in translation processes. Citing various scholars, it argues that translation is more than a simple transfer of text; it reflects power dynamics, cultural politics, and the manipulation of knowledge between cultures. The essays analyze the impact of translation on societal perceptions of culture and literature, stressing the necessity for translators to acknowledge their role in shaping meaning and the implications of their choices.

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