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Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
Lázaro Emílio Makili, Jesús A. Vega Sánchez, Sebastián Dormido-Canto
Fusion Science and Technology | Volume 62 | Number 2 | October 2012 | Pages 347-355
Selected Paper from the Seventh Fusion Data Validation Workshop 2012 (Part 1) | doi.org/10.13182/FST12-A14626
Articles are hosted by Taylor and Francis Online.
This paper addresses the problem of finding a minimal and good enough training data set for classification purposes by using active learning and conformal predictors. Active learning means to have control in the selection process of training samples instead of choosing them in a random way. To this end, active learning methodologies look for establishing selection criteria in order to find out the samples that show better discrimination capabilities. In the present case, conformal predictors have been used for these purposes. Results will be presented in a five-class classification problem with images. The features are the vertical detail coefficients of the Haar wavelet transform at level four to diminish the sample dimensionality by reducing the spatial redundancy of the images. The active selection of training sets (through the reliability measures of a conformal predictor) allows the improvement of the classifiers. Here, the word "improvement" refers to obtaining higher generalization properties thereby avoiding overfitting. Support vector machines classifiers, in the one-versus-the-rest approach, have been used as the underlying classifiers.