AbstractA class of computable functions ismaximaliff it can be incrementally learned by some inductive inference machine (IIM), but no infinitely larger class of computable functions can be so learned. Rolf Wiehagen posed the question whether there exist such maximal classes. This question and many interesting variants are answered herein in the negative. Viewed positively, each IIM can be infinitely improved upon! Also discussed are the problems of algorithmically finding the improvements proved to exist
Abstract. We explore two analogies between computability theory and a basic model of learning, namel...
We will describe recent developments in a system for machine learning that we’ve been working on for...
AbstractIn this paper we study the question of whether identifiable classes have subclasses which ar...
This thesis analyses the limits placed on computation by Barzdin's lemmas, and discusses their impli...
Inductive inference machines are algorithmic devices which attempt to synthesize (in the limit) prog...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
Identification of programs for computable functions from their graphs by algorithmic devices is a we...
AbstractMost theories of learning consider inferring a function f from either (1) observations about...
Machine learning researchers and practitioners steadily enlarge the multitude of successful learning...
AbstractGoldʼs original paper on inductive inference introduced a notion of an optimal learner. Intu...
Abstract. We explore two analogies between computability theory and a basic model of learning, namel...
We will describe recent developments in a system for machine learning that we’ve been working on for...
AbstractIn this paper we study the question of whether identifiable classes have subclasses which ar...
This thesis analyses the limits placed on computation by Barzdin's lemmas, and discusses their impli...
Inductive inference machines are algorithmic devices which attempt to synthesize (in the limit) prog...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
Identification of programs for computable functions from their graphs by algorithmic devices is a we...
AbstractMost theories of learning consider inferring a function f from either (1) observations about...
Machine learning researchers and practitioners steadily enlarge the multitude of successful learning...
AbstractGoldʼs original paper on inductive inference introduced a notion of an optimal learner. Intu...
Abstract. We explore two analogies between computability theory and a basic model of learning, namel...
We will describe recent developments in a system for machine learning that we’ve been working on for...
AbstractIn this paper we study the question of whether identifiable classes have subclasses which ar...