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→{{anchor|Generalization}} 理论 Theory
In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
除了性能界限，学习理论家们还研究学习的时间复杂性和可行性。在机器学习理论中，若一个计算被认为是可行的，当且仅当它可以在复杂度为多项式的时间范围内完成。关于时间复杂度的结果有两种。''正样结果 Negative results''表明，有些模式的函数可以在多项式时间复杂度内学习到。但同时''负样结果 Negative results''表明，某些模式并不能在多项式时间内学习到。
== Approaches ==
== Approaches ==