因为用户的参与度是不可预计的，所以我们使用了另外一种方式，我们使用主动学习技术，通过分析用户的交互信息来对算法进行提升。

Since user engagement is a variable that we cannot predetermine, we instead use active learning techniques to apply those interactions (where they exist) to the improvement of the algorithm.

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为了提高性别检测的精度，提出了一种支持向量机(SVM)与主动外观模型(aam)相结合的迭代学习算法。

In order to increase accuracy in gender classification, an iterative learning approach combining support vector machine (SVM) and active appearance model (AAM) was proposed.

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该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。

The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.

youdao

该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。

The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.

youdao