Separating models of learning with faulty teachers

Separating Models of Learning with Faulty Teachers

We study the power of two models of faulty teachers in Valiant’s PAC learning model and Angluin’s exact learning model. The first model we consider is learning from an incomplete membership oracle introduced by Angluin and Slonim (1994). In this model, the answers to a random subset of the learner’s membership queries may be missing. The second model we consider is random persistent classificat...

a comparison of teachers and supervisors, with respect to teacher efficacy and reflection

supervisors play an undeniable role in training teachers, before starting their professional experience by preparing them, at the initial years of their teaching by checking their work within the proper framework, and later on during their teaching by assessing their progress. but surprisingly, exploring their attributes, professional demands, and qualifications has remained a neglected theme i...

Separating Models of Learning from Correlated and Uncorrelated Data

We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possible examples. Previous research has suggested that the Random Walk model is more powerful than comparable standard models of learning from independent examples, by exhibiting learning algorithms in the Random Walk framew...

Separating Distribution-Free and Mistake-Bound Learning Models

Separating Style and Content with Bilinear Models

Perceptual systems routinely separate "content" from "style," classifying familiar words spoken in an unfamiliar accent, identifying a font or handwriting style across letters, or recognizing a familiar face or object seen under unfamiliar viewing conditions. Yet a general and tractable computational model of this ability to untangle the underlying factors of perceptual observations remains elu...