自动功能、线性时间和学习
Automatic functions, linear time and learning
摘要：
目前的工作确定的确切性质{ \em线性时间可计算}的概念，其中包括自动功能（其图形是由有限自动机识别）。本文还确定了哪种类型的线性时间概念允许充分的学习能力在有限的自动类（家庭的语言是一致公认的有限自动机）。特别是它表明，一个功能是自动的，如果有一个磁带图灵机的左端计算的功能在线性时间的输入计算和输出计算后都开始在左端。众所周知，学习者意识到自动更新功能对学习有限制。在目前的工作中，它表明，可以克服这个问题，通过提供额外的资源有限的基带工作磁带，同时保持更新时间是线性的最大数据的长度到目前为止。在这个模型中，一个额外的这样的工作带提供了额外的学习能力超过自动学习器模型，两个额外的工作带提供了完整的学习能力。此外，还可以考虑附加的队列或附加的栈来代替附加的工作带，并且对于这些设备，一个队列或两个栈足以用于完全学习能力，而一个栈是不够的。
英文摘要：
The present work determines the exact nature of {\em linear time computable} notions which characterise automatic functions (those whose graphs are recognised by a finite automaton). The paper also determines which type of linear time notions permit full learnability for learning in the limit of automatic classes (families of languages which are uniformly recognised by a finite automaton). In particular it is shown that a function is automatic iff there is a one-tape Turing machine with a left end which computes the function in linear time where the input before the computation and the output after the computation both start at the left end. It is known that learners realised as automatic update functions are restrictive for learning. In the present work it is shown that one can overcome the problem by providing work tapes additional to a resource-bounded base tape while keeping the update-time to be linear in the length of the largest datum seen so far. In this model, one additional such work tape provides additional learning power over the automatic learner model and two additional work tapes give full learning power. Furthermore, one can also consider additional queues or additional stacks in place of additional work tapes and for these devices, one queue or two stacks are sufficient for full learning power while one stack is insufficient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
