It is well-known that Naive Bayes can only represent linearly separable functions in binary domains. But the learnability of general Augmented Naive Bayes is open. Little work is done on the learnability of Bayesian networks in nominal domains, a general case of binary domains. This paper explores the learnability of Augmented Naive Bayes in nominal domains. We introduce a complexity measure fo...

Most studies were devoted to the design of efficient algorithms and the evaluation and application on diverse ranking problems, whereas few work has been paid to the theoretical studies on ranking learnability. In this paper, we study the relation between uniform convergence, stability and learnability of ranking. In contrast to supervised learning where the learnability is equivalent to unifor...

In this paper, a new notion of learnability is introduced, referred to as learnability with prior information (w.p.i.). This notion is weaker than the standard notion of probably approximately correct (PAC) learnability which has been much studied during recent years. A property called “dispersability” is introduced, and it is shown that dispersability plays a key role in the study of learnabil...

In this paper, we consider some problems in learning with respect to a fixed distribution. We introduce two new notions of learnability; these are probably uniformly approximately correct (PUAC) learnability which is a stronger requirement than the widely studied PAC learnability, and minimal empirical risk (MER) learnability, which is a stronger requirement than the previously defined notions ...

The aim of the research is to derive a framework for the evaluation of Computer Aided Software Engineering (CASE) tool learnability in educational environments. Drawing from the literature of Human Computer Interaction and educational research, a framework for evaluating CASE tool learnability in educational environments is derived. The two main differences between this framework and existing e...