Add to ORKGIt is proved that the UCEM property of a family of measurable functions F implies that F is totally bounded; the UCEMUP property and PAC learnability still preserve when the family of probabilities is replaced by its closure. And a concept class C is constructed to show that every PAC algorithm of C would require a super-polynomial number of samples. Finally, the learnability of a concept class C with respect to the probability measures P and its convex hull C(P) is discussed and a mistake of [1] is corrected.EI03347-3573
We define a new PAC learning model. In this model, examples are drawn according to the universal dis...
Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distr...
Abstract. The PAC and other equivalent learning models are widely accepted models for polynomial lea...
证明了如果函数族F具有UCEM性质,那么F是完全有界的.此外如果F关于概率族P是PAC可学习的或具有UCEM性质,则F关于P的闭包也具有同样的性质.构造了一个非多项式可学习的例子,说明了PAC可学习的...
Learnability in Valiant's PAC learning model has been shown to be strongly related to the exist...
AbstractValiant's protocol for learning is extended to the case where the distribution of the exampl...
AbstractThis paper focuses on a general setup for obtaining sample size lower bounds for learning co...
We narrow the width of the confidence interval introduced by Vapnik and Chervonenkis for the risk fu...
We present a new perspective for investigating the Probably Approximate Correct (PAC) learnability o...
This paper focuses on a general setup for obtaining sample size lower bounds for learning concept cl...
We consider some problems in learning with respect to a fixed distribution. We introduce two new not...
A PAC teaching model -under helpful distributions -is proposed which introduces the classical ideas...
In this paper we study a new restriction of the PAC learning framework, in which each label class is...
We prove that the class of convex bodies contained in a fixed (prescribed) bounded region R c lQd is...
AbstractWe present a new perspective for investigating the probably approximate correct (PAC) learna...
We define a new PAC learning model. In this model, examples are drawn according to the universal dis...
Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distr...
Abstract. The PAC and other equivalent learning models are widely accepted models for polynomial lea...
证明了如果函数族F具有UCEM性质,那么F是完全有界的.此外如果F关于概率族P是PAC可学习的或具有UCEM性质,则F关于P的闭包也具有同样的性质.构造了一个非多项式可学习的例子,说明了PAC可学习的...
Learnability in Valiant's PAC learning model has been shown to be strongly related to the exist...
AbstractValiant's protocol for learning is extended to the case where the distribution of the exampl...
AbstractThis paper focuses on a general setup for obtaining sample size lower bounds for learning co...
We narrow the width of the confidence interval introduced by Vapnik and Chervonenkis for the risk fu...
We present a new perspective for investigating the Probably Approximate Correct (PAC) learnability o...
This paper focuses on a general setup for obtaining sample size lower bounds for learning concept cl...
We consider some problems in learning with respect to a fixed distribution. We introduce two new not...
A PAC teaching model -under helpful distributions -is proposed which introduces the classical ideas...
In this paper we study a new restriction of the PAC learning framework, in which each label class is...
We prove that the class of convex bodies contained in a fixed (prescribed) bounded region R c lQd is...
AbstractWe present a new perspective for investigating the probably approximate correct (PAC) learna...
We define a new PAC learning model. In this model, examples are drawn according to the universal dis...
Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distr...
Abstract. The PAC and other equivalent learning models are widely accepted models for polynomial lea...