Incremental 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 and this example x is not presented to the learner again thereafter, then the learner will forget x, i.e., 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. 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 lang...

• Type: Journal
• Year: 2010
• Where: TCS
• Authors: Sanjay Jain, Steffen Lange, Samuel E. Moelius, Sandra Zilles
• Keywords: Example Memory, Inductive Inference, Model, TCS 2010, Theoretical Computer Science