The rate of technological progression seems to have encouraged teachers and researchers to reassess their pedagogic beliefs in an attempt to maintain their control over the learning process and the influx of authentic materials. With regard to the field of language teaching, technology seems to be demanding a re-examination of pedagogies in order to find ways of making use of current technology...

The paper describes a learner corpus of Czech, currently under development. The corpus captures Czech as used by nonnative speakers. We discuss its structure, the layered annotation of errors and the annotation process.

Language learners must learn the meanings of many thousands of words, despite those words occurring in complex environments in which infinitely many meanings might be inferred by the learner as a word's true meaning. This problem of infinite referential uncertainty is often attributed to Willard Van Orman Quine. We provide a mathematical formalisation of an ideal cross-situational learner attem...

We consider the supervised learning of a binary classifier from noisy observations. We use smooth boosting to linearly combine abstaining hypotheses, each of which maps a subcube of the attribute space to one of the two classes. We introduce a new branch-and-bound weak learner to maximize the agreement rate of each hypothesis. Dobkin et al. give an algorithm for maximizing agreement with real-v...

AdaBoost is a popular and eeective leveraging procedure for improving the hypotheses generated by weak learning algorithms. AdaBoost and many other leveraging algorithms can be viewed as performing a constrained gradient descent over a potential function. At each iteration the distribution over the sample given to the weak learner is the direction of steepest descent. We introduce a new leverag...

We focus on the adaptation of boosting to representation spaces composed of different subsets of features. Rather than imposing a single weak learner to handle data that could come from different sources (e.g., images and texts and sounds), we suggest the decomposition of the learning task into several dependent sub-problems of boosting, treated by different weak learners, that will optimally c...

We present a theory of boosting probabilistic classifiers. We place ourselves in the situation of a user who only provides a stopping parameter and a probabilistic weak learner/classifier and compare three types of boosting algorithms: probabilistic Adaboost, decision tree, and tree of trees of ... of trees, which we call matryoshka. “Nested tree,” “embedded tree” and “recursive tree” are also ...

The present paper deals with strong-monotonic, monotonic and weak-monotonic language learning from positive and negative examples. The three notions of monotonicity re ect di erent formalizations of the requirement that the learner has to produce always better and better generalizations when fed more and more data on the concept to be learnt. We characterize strong-monotonic, monotonic, weak-mo...