Incremental learning with temporary memory

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 s...

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 s...

Learning with Temporary Memory (Expanded Version)

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 s...

Incremental Learning with Partial Instance Memory

Incremental Rule Learning with Partial Instance Memory for Changing Concepts

Learning concepts that change over time is important for a variety of applications in which an intelligent system must acquire and use a behavioral profile. Computer intrusion detection, calendar scheduling, and intelligent user interfaces are three examples. An interesting class of methods for learning such concepts consists of algorithms that maintain a portion of previously encountered examp...