We investigate a variant of the on-line learning model for classes
of {0,1}-valued functions (concepts) in which the labels of a certain
amount of the input instances are corrupted by adversarial noise.
We propose an extension of a general learning strategy, known as
"Closure Algorithm", to this noise ... more >>>

Littlestone developed a simple deterministic on-line learning
algorithm for learning $k$-literal disjunctions. This algorithm
(called Winnow) keeps one weight for each variable and does
multiplicative updates to its weights. We develop a randomized
version of Winnow and prove bounds for an adaptation of the
algorithm ... more >>>

We present a PAC-learning algorithm and an on-line learning algorithm
for nested differences of intersection-closed classes. Examples of
intersection-closed classes include axis-parallel rectangles,
monomials, and linear sub-spaces. Our PAC-learning algorithm uses a
pruning technique that we rigorously proof correct. As a result we
show that ... more >>>