Add to ORKGA learner noisily infers a function or set, if every correct item is presented infinitely often while in addition some incorrect data ("noise") is presented a finite number of times. It is shown that learning from a noisy informant is equal to finite learning with K-oracle from a usual informant. This result has several variants for learning from text and using different oracles. Furthermore, partial identification of all r.e. sets can cope also with noisy input
Learning from noisy data is very difficult. But if a certain method fails people often try again - i...
We introduce a theoretical model of information acquisition under resource limitations in a noisy en...
Learning from noisy data is very difficult. But if a certain method fails people often try again - i...
AbstractThe present paper deals with several variants of inductive inference from noisy data. The no...
International audienceTo study the problem of learning from noisy data, the common approach is to us...
International audienceTo study the problem of learning from noisy data, the common approach is to us...
AbstractThe present work employs a model of noise introduced earlier by the third author. In this mo...
The present work investigates Gold style algorithmic learning from inputoutput examples whereby the...
AbstractThe present work investigates Gold-style algorithmic learning from input–output examples whe...
Abstract — An active learner is given an instance space, a label space and a hypothesis class, where...
We consider formal models of learning from noisy data. Specifically, we focus on learning in the pro...
In regression learning, it is often difficult to obtain the true values of the label variables, whil...
This paper presents an approach to learning from noisy data that views the problem as one of reasoni...
The combination of two major challenges in machine learning is investi-gated: dealing with large amo...
Abstract. In regression learning, it is often difficult to obtain the true values of the label varia...
Learning from noisy data is very difficult. But if a certain method fails people often try again - i...
We introduce a theoretical model of information acquisition under resource limitations in a noisy en...
Learning from noisy data is very difficult. But if a certain method fails people often try again - i...
AbstractThe present paper deals with several variants of inductive inference from noisy data. The no...
International audienceTo study the problem of learning from noisy data, the common approach is to us...
International audienceTo study the problem of learning from noisy data, the common approach is to us...
AbstractThe present work employs a model of noise introduced earlier by the third author. In this mo...
The present work investigates Gold style algorithmic learning from inputoutput examples whereby the...
AbstractThe present work investigates Gold-style algorithmic learning from input–output examples whe...
Abstract — An active learner is given an instance space, a label space and a hypothesis class, where...
We consider formal models of learning from noisy data. Specifically, we focus on learning in the pro...
In regression learning, it is often difficult to obtain the true values of the label variables, whil...
This paper presents an approach to learning from noisy data that views the problem as one of reasoni...
The combination of two major challenges in machine learning is investi-gated: dealing with large amo...
Abstract. In regression learning, it is often difficult to obtain the true values of the label varia...
Learning from noisy data is very difficult. But if a certain method fails people often try again - i...
We introduce a theoretical model of information acquisition under resource limitations in a noisy en...
Learning from noisy data is very difficult. But if a certain method fails people often try again - i...