In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
在统计建模中，有很多分类器构建算法，每个算法构造一组不同的关于数据的假设集合。
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.
为了获得最好的模型性能，挑选做出最合适假设的建模算法—而不只是选择你最熟悉那个算法，是很重要的。
With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.
随着数据集的数据量和维数的增加，建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。
Moreover, the amount of examples needed to build a reliable classifier by statistical means is much larger than it is available for humans.
况且，通过统计手段来建立一个可靠的分类器，对于人类来说需要非常巨大的用例数目。
In this paper, we introduce four boolean kernels. Using the four kernel functions which we introduce, we build a novelty decision rule classifier named as DRC-BK.
本文介绍了四种具有归纳偏置的布尔核函数，我们利用这 四种核函数构造出一种新颖的决策规则分类器DRC-BK。
In this paper, we introduce four boolean kernels. Using the four kernel functions which we introduce, we build a novelty decision rule classifier named as DRC-BK.
本文介绍了四种具有归纳偏置的布尔核函数，我们利用这 四种核函数构造出一种新颖的决策规则分类器DRC-BK。
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