The perceptron algorithm training

A question concerning the perceptron training is how many iterations over the training examples gives the best output accuracy.

Some criteria can be used. For example, the number of iterations can be fixed from the development tests. Alternatively, a set of examples can be taken out of the training data from the test set, and using those for testing and the rest for training, the number of iterations determined by the corresponding observations. Still an alternative is to check the training error. When the training error is below a certain proportion of the training data, the learning process stops. The last method assumes that the perceptron model converges.

I have seen some example where the first method is far from optimal. This happens in a case where the size difference between the development data and the test data is huge. It seems that the second method is a comparatively sensible choice.

some examples to illustrate that Chinese is more semantic than syntactic

Try to do the following sentence comprehensions:

(1.1) 我挠的他很痒
Who is itchy? (a) 我 (b) 他

(1.2) 我找的他很苦
Who is anxious? (a) 我 (b) 你

(2.1) 我们大胜对手
Who won? (a) 我们 (b) 对手

(2.2) 我们大败对手
Who won? (a) 我们 (b) 对手

(3.1) 他高中，我本科。
What verb is omitted?

(3.2) 他宝马，我夏利
What verb is omitted?

(3.3) 他志强，我志国
What verb is omitted?

(4.1) 他吃面包
What did he eat?

(4.2) 他吃饭馆
What did he eat?

(5.1) 我们擦玻璃了
Who did what to who?

(5.2) 地板擦干净了
Who did what to who?