We provide tight upper and lower bounds on the expected minimum Kolmogorov complexity of binary classifiers that are consistent with labeled samples. The expected size is not more than complexity of the target concept plus the conditional entropy of the labels given the sample.

翻译：对于符合标签样本的二进制分类器的预期最低科尔莫戈洛夫复杂性,我们提供了严格的上下界限。 预计的尺寸不超过目标概念的复杂性,加上根据样本设定的标签的有条件倍增。