RESUMEN
Darwin's first two, relatively complete, explicit articulations of his theorizing on evolution were his Essay of 1844 and On the Origin of Species published in 1859. A comparative analysis concludes that they espoused radically different theories despite exhibiting a continuity of strategy, much common structure and the same key idea. Both were theories of evolution by means of natural selection. In 1844, organic adaptation was confined to occasional intervals initiated and controlled by de-stabilization events. The modified descendants rebalanced the particular "plant and animal forms unsettled by some alteration in their circumstances." But by 1859, organic adaptation occurred continuously, potentially modifying the descendants of all organisms. Even natural selection, the persistent core of Darwin's theorizing, does not prove to be a significant basis for theory similarity. Consequently, Darwin's Origin theory cannot reasonably be considered as a mature version of the Essay. It is not a modification based on adjustments, further justifications and the integration of a Principle of Divergence. The Origin announced a new "scientific paradigm" while the Essay did little more than seemingly misconfigure the operation of a novel mechanism to extend varieties beyond their accepted bounds, and into the realm of possible new species. Two other collections of Darwin's theorizing are briefly considered: his extensive notes of the late 1830s and his contributions to the famous meeting of 1 July 1858. For very different reasons, neither constitutes a challenge to the basis for this comparative study. It is concluded that, in addition to the much-debated social pressures, an unacknowledged further reason why Darwin did not publish his theorizing until 1859, could have been down to his perceptive technical judgement: wisely, he held back from rushing to publish demonstrably flawed theorizing.
Asunto(s)
Evolución Biológica , Historia Natural/historia , Selección Genética , Biología/historia , Historia del Siglo XIXRESUMEN
Bayesian averaging (BA) over ensembles of decision models allows evaluation of the uncertainty of decisions that is of crucial importance for safety-critical applications such as medical diagnostics. The interpretability of the ensemble can also give useful information for experts responsible for making reliable decisions. For this reason, decision trees (DTs) are attractive decision models for experts. However, BA over such models makes an ensemble of DTs uninterpretable. In this paper, we present a new approach to probabilistic interpretation of Bayesian DT ensembles. This approach is based on the quantitative evaluation of uncertainty of the DTs, and allows experts to find a DT that provides a high predictive accuracy and confident outcomes. To make the BA over DTs feasible in our experiments, we use a Markov Chain Monte Carlo technique with a reversible jump extension. The results obtained from clinical data show that in terms of predictive accuracy, the proposed method outperforms the maximum a posteriori (MAP) method that has been suggested for interpretation of DT ensembles.
Asunto(s)
Algoritmos , Inteligencia Artificial , Teorema de Bayes , Sistemas de Apoyo a Decisiones Clínicas , Técnicas de Apoyo para la Decisión , Diagnóstico por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Método de MontecarloRESUMEN
The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images.