Naive Bayes classifiers often break down when the size of the training examples per class are not balanced or when the data is not independent enough.
当各类的训练示例的大小不平衡，或者数据的独立性不符合要求时，Naive Bayes分类器会出现故障。
This method greatly reduces the number of training examples and so improves the training speed of SVM, without the loss in generalization performance.
在保证支撑矢量机的分类能力情况下，该方法大大地减少了训练样本的个数，同时提高了支撑矢量机的训练速度。
The key problem of training support vector machines is how to solve quadratic programming problem, but for large training examples, the problem is too difficult.
训练支持向量机的本质问题就是求解二次规划问题，但对大规模的训练样本来说，求解二次规划问题困难很大。
Concept learning can be formulated as a problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples.
概念学习可以看作是一个搜索过程，它在预定义的假设空间中按着某种搜索策略进行搜索，使学到的概念与训练样例有最佳的拟合度。
Concept leaning is to induce the general rules from specific training examples and the rules are expected to both cover the positive examples and exclude the negative examples.
概念学习是指从特殊的训练样例中归纳出覆盖正例排斥反例的一般规律。
We can learn a set of fuzzy production rules which are learned from training examples have poor reasoning accuracy with respect to the training examples.
我们从训练事例中可以学习得到一组模糊产生规则，但这组规则对于相应训练事例的推理精度一般都有待提高。
We can learn a set of fuzzy production rules which are learned from training examples have poor reasoning accuracy with respect to the training examples.
我们从训练事例中可以学习得到一组模糊产生规则，但这组规则对于相应训练事例的推理精度一般都有待提高。
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