Learning with Temporary Memory

In the inductive inference framework of learning in the limit, a variation of the bounded example memory (Bem) language learning model is considered. Intuitively, the new model constrains the learner’s memory not only in how much data may be retained, but also in how long that data may be retained. More specifically, the model requires that, if a learner commits an example x to memory in some stage of the learning process, then there is some subsequent stage for which x no longer appears in the learner’s memory. This model is called temporary example memory (Tem) learning. In some sense, it captures the idea that memories fade. Many interesting results concerning the Tem-learning model are presented. For example, there exists a class of languages that can be identified by memorizing k + 1 examples in the Tem sense, but that cannot be identified by memorizing k examples in the Bem sense. On the other hand, there exists a class of languages that can be identified by memorizing ju...

• Type: Conference
• Year: 2008
• Where: ALT
• Authors: Steffen Lange, Samuel E. Moelius, Sandra Zilles
• Keywords: ALT 2008, Bem Sense, Machine Learning, Tem Sense, Temporary Example Memory