Add to ORKGThis report surveys some key results on the learning of Boolean functions in a probabilistic model that is a generalization of the well-known ‘PAC’ model. A version of this is to appear as a chapter in a book on Boolean functions, but the report itself is relatively self-contained
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
We consider a fundamental problem in computational learn-ing theory: learning an arbitrary Boolean f...
In Machine Learning (ML) and Evolutionary Computation (EC), it is often beneficial to approximate a ...
This report surveys some key results on the learning of Boolean functions in a probabilistic model t...
This report surveys some key results on the learning of Boolean functions in a probabilistic model t...
Abstract—In this paper, we analyze Boolean functions using a re-cently proposed measure of their com...
We analyze Boolean functions using a recently proposed measure of their complexity. This complexity ...
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Valiant (1984) and others have studied the problem of learning vari-ous classes of Boolean functions...
We describe some recent or not-so-recent results in the model of learning known as exact learning f...
We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean f...
AbstractWe consider a fundamental problem in computational learning theory: learning an arbitrary Bo...
We narrow the width of the confidence interval introduced by Vapnik and Chervonenkis for the risk fu...
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
AbstractWe give an overview of the fastest known algorithms for learning various expressive classes ...
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
We consider a fundamental problem in computational learn-ing theory: learning an arbitrary Boolean f...
In Machine Learning (ML) and Evolutionary Computation (EC), it is often beneficial to approximate a ...
This report surveys some key results on the learning of Boolean functions in a probabilistic model t...
This report surveys some key results on the learning of Boolean functions in a probabilistic model t...
Abstract—In this paper, we analyze Boolean functions using a re-cently proposed measure of their com...
We analyze Boolean functions using a recently proposed measure of their complexity. This complexity ...
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Valiant (1984) and others have studied the problem of learning vari-ous classes of Boolean functions...
We describe some recent or not-so-recent results in the model of learning known as exact learning f...
We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean f...
AbstractWe consider a fundamental problem in computational learning theory: learning an arbitrary Bo...
We narrow the width of the confidence interval introduced by Vapnik and Chervonenkis for the risk fu...
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
AbstractWe give an overview of the fastest known algorithms for learning various expressive classes ...
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
We consider a fundamental problem in computational learn-ing theory: learning an arbitrary Boolean f...
In Machine Learning (ML) and Evolutionary Computation (EC), it is often beneficial to approximate a ...