This paper presents a training algorithm for probabilistic neural networks using the MCE criterion.

提出了一种基于最小分类错误准则的概率神经网络的训练算法。

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A corollary of this principle is that a learning algorithm should never be evaluated for its results in the training set because this shows no evidence of an ability to generalize to unseen instances.

这个原理的一个推论是，一种学习算法永远不会对它训练集的结果进行评估，因为对于一种未知的事例而言，没有证据表明算法具有概括它们的能力。

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This gives you a much larger training set for each trial, meaning that your algorithm will have enough data to learn from, but it also gives a fairly large number of tests (20 instead of 5 or 10).

对每次尝试来说，训练集都非常大，这意味着你的算法有足够的数据进行学习，而且这样一来也提供了足够多的测试次数(20次，而不是5次或10次)。

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E. you read a simple input csv, split it into training and test set, run a simple algorithm with readily-tuneable or explorable hyperparameters, and a simple output of relevant statistics.

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