The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Exact learning of half-spaces over finite subsets of ℝn from membership queries is considered. We describe the minimum set of labelled examples separating the target concept from all the other ones of the concept class under consideration. For a domain consisting of all integer points of some polytope we give non-trivial lower bounds on the complexity of exact identification of half-spaces. These...
We assume wlog. that every learning algorithm with membership and equivalence queries proceeds in rounds. In each round it puts in parallel a polynomial number of queries and after receiving the answers, it performs internal computations before starting the next round. The query depth is defined by the number of rounds. In this paper we show that, assuming the existence of cryptographic one-way functions,...
We investigate the learning problem of unary output two-tape non deterministic finite automata (unary output 2-tape NFAs) from multiplicity and equivalence queries. Given an alphabet A and a unary alphabet x, a unary output 2-tape NFA accepts a subset of AA*xx*. In [6] Bergadano and Varricchio proved that the behavior of an unknown automaton with multiplicity in a field K (K-automaton)...
We address the problem of nonadaptive learning of Boolean functions with few relevant variables by membership queries. In another recent paper [7] we have characterized those assignment families (query sets) which are sufficient for nonadaptive learning of this function class, and we studied the query number. However, the reconstruction of the given Boolean function from the obtained responses is...
Learning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account,but in most cases it fails to describe such kind of learning. We show that in order to make the learning from positive data possible, extra-information about the underlying distribution must be provided to the learner. We define a PAC learning...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.