In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given grammatical module. We decompose the learning problem and present formal results for a central subp...

This paper explores a PAC (probably approximately correct) learning model in cooperative games. Specifically, we are given m random samples of coalitions and their values, taken from some unknown cooperative game; can we predict the values of unseen coalitions? We study the PAC learnability of several well-known classes of cooperative games, such as network flow games, threshold task games, and...

We present a series of theoretical and experimental results on the learnability of description logics. We rst extend previous formal learnability results on simple description logics to C-Classic, a description logic expressive enough to be practically useful. We then experimentally evaluate two extensions of a learning algorithm suggested by the formal analysis. The rst extension learns C-Clas...

It is well known that the naive Bayesian classiier is linear in binary domains. However, little work is done on the learnability of the naive Bayesian classiier in nominal domains, a general case of binary domains. This paper explores the geometric properties of the naive Bayesian classiier in nominal domains. First we propose a three-layer measure for the linearity of functions in nominal doma...

How does the existence of case systems, and strict word order patterns affect the learnability of a given language? We present a series of connectionist simulations, suggesting that both case and strict word order may facilitate syntactic acquisition by a sequential learning device. Our results are consistent with typological data concerning the frequencies with which different type of word ord...

We establish quantitative methods for comparing and estimating the quality of dependency annotations or conversion schemes. We use generalized tree-edit distance to measure divergence between annotations and propose theoretical learnability, derivational perplexity and downstream performance for evaluation. We present systematic experiments with treeto-dependency conversions of the PennIII tree...

Over recent years, more and more online coursewares have been released to facilitate people’s online learning experience. Although these courseware systems have undergone intensive development over the years, course sites developed from these courseware systems are not userfriendly to students. In addition, the designs of those online educational courseware systems often do not embody particula...

The use of Computer Aided Software Engineering (CASE) tools for teaching object-oriented systems analysis and design (OOSAD) and the Unified Modelling Language (UML) has many potential benefits, but there are several problems associated with the usability and learnability of these tools. This paper describes a study undertaken to determine if computing students from a linguistically and technol...

We consider the problem of sequential prediction and provide tools to study the minimax value of the associated game. Classical statistical learning theory provides several useful complexity measures to study learning with i.i.d. data. Our proposed sequential complexities can be seen as extensions of these measures to the sequential setting. The developed theory is shown to yield precise learni...