因此，要运行Mahout的分类器，您首先需要训练模式，然后再使用该模式对新内容进行分类。
Thus, to run Mahout's classifier, you need to first train the model and then use that model to classify new content.
模式识别问题不仅与表示方法有关，也跟用于在分类器设计各个阶段进行训练和测试的用例集有关。
A pattern recognition problem is not only specified by a representation, but also by the set of examples given for training and evaluating a classifier in various stages.
前向网络在用于模式分类时，其网络的有效训练一直是一个受到关注的问题。
It has payed great attention to effective training of feedforward neural networks when they are used for pattern classification.
基于梯度下降的神经网络训练算法易于陷入局部最小，从而使网络不能对输入模式进行准确分类。
Neural network BP training algorithm based on gradient descend technique may lead to entrapment in local optimum so that the network inaccurately classifies input patterns.
提出了一种基于最小分类错误(MCE)训练的采用多层感知器(MLP)结构的模式分类器设计方法。
An improved design method on pattern classifier based on multi-layer perceptrons (MLP) by means of minimum classification error (MCE) training was proposed.
提出一种从训练样本提取基于超盒表示的模糊规则的方法，用于模式分类。
In this paper, we discuss a new method for rule extraction based on hyper-box representation.
提出一种从训练样本提取基于超盒表示的模糊规则的方法，用于模式分类。
In this paper, we discuss a new method for rule extraction based on hyper-box representation.
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