Results 1 
2 of
2
Recursion Theoretic Models of Learning: Some Results and Intuitions
 Annals of Mathematics and Artificial Intelligence
, 1995
"... View of Learning To implement a program that somehow "learns" it is neccessary to fix a set of concepts to be learned and develop a representation for the concepts and examples of the concepts. In order to investigate general properties of machine learning it is neccesary to work in as representati ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
View of Learning To implement a program that somehow "learns" it is neccessary to fix a set of concepts to be learned and develop a representation for the concepts and examples of the concepts. In order to investigate general properties of machine learning it is neccesary to work in as representation independent fashion as possible. In this work, we consider machines that learn programs for recursive functions. Several authors have argued that such studies are general enough to include a wide array of learning situations [2,3,22,23,24]. For example, a behavior to be learned can be modeled as a set of stimulus and response pairs. Assuming that any behavior associates only one response to each possible stimulus, behaviors can be viewed as functions from stimuli to responses. Some behaviors, such as anger, are not easily modeled as functions. Our primary interest, however, concerns the learning of fundamental behaviors such as reading (mapping symbols to sounds), recognition (mapping pa...
Learning via Queries with Teams and Anomalies
"... this paper we continue the study of machines that ask questions by comparing such machines to teams of passive machines [26]. This yields, via work of Pitt and Smith [19], a comparison of active learning with probabilistic learning [18]. Also considered are query inference machines that learn an app ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
this paper we continue the study of machines that ask questions by comparing such machines to teams of passive machines [26]. This yields, via work of Pitt and Smith [19], a comparison of active learning with probabilistic learning [18]. Also considered are query inference machines that learn an approximation of what is desired. The approximation differs from the desired result in finitely many anomalous places. Passive approximate inductive inference has been extensively investigated [8,10,11,21,27].