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Combining Discriminant models with New Multi-Class SVMs

机译：将判别模型与新的多类SVM相结合

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摘要

The idea of performing model combination, instead of model selection, has a long theoretical background in statistics. However, making use of theoretical results is ordinarily subject to the satisfaction of strong hypotheses (weak error correlation, availability of large training sets, possibility to rerun the training procedure an arbitrary number of times, etc.). in contrast, the practitioner is frequently faced with the problem of combining a given set of pre-trained classifiers, with highly correlated errors, using only a small training sample. Overfitting is then the main risk, which cannot be overcome but with a strict complexity control of the combiner selected.
机译：执行模型组合而不是模型选择的想法在统计学上具有悠久的理论背景。但是，利用理论结果通常要满足强假设（弱错误相关性，大量训练集的可用性，任意次重新运行训练过程的可能性等）。相反，从业者经常面临仅使用少量训练样本就将给定的一组预先训练的分类器与高度相关的误差相结合的问题。因此，过度拟合是主要风险，只有对所选组合器进行严格的复杂性控制，这是无法克服的。

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