We present a membership-query algorithm for eeciently learning DNF with respect to the uniform distribution. In fact, the algorithm properly learns with respect to uniform the class TOP of Boolean functions expressed as a majority vote over parity functions. We also describe extensions of this algorithm for learning DNF over certain nonuniform distributions and for learning a class of geometric...

We present a membership-query algorithm for ef i ciently learning DNF with respect to the uniform distribution. In fact, the algorithm properly learns the more general class of functions that are computable as a majority of polynomially-many parity functions. We also describe extensions of this algorithm for learning DNF over certain nonuniform distributions and from noisy examples as well as f...

In propositional learning, boosting has been a very popular technique for increasing the accuracy of classification learners. In firstorder learning, on the other hand, surprisingly little attention has been paid to boosting, perhaps due to the fact that simple forms of boosting lead to loss of comprehensibility and are too slow when used with standard ILP learners. In this paper, we show how b...

We investigate AdaBoost and bipartite version of RankBoost abilities to minimize AUC and its application for score level fusion in multimodal biometric systems. To do this, we customize two methods of weak learner training. Empirical results show comparable AUC for AdaBoost and RankBoost.B which previously was addressed theoretically. We demonstrate exhaustive results among state of the art cla...

This paper presents an efficient method for automatic training of performant visual object detectors, and its successful application to training of a back-view car detector. Our method for training detectors is adaBoost applied to a very general family of visual features (called “control-point” features), with a specific feature-selection weak-learner: evo-HC, which is a hybrid of Hill-Climbing...

This work introduces a transformation-based learner model for classification forests. The weak learner at each split node plays a crucial role in a classification tree. We propose to optimize the splitting objective by learning a linear transformation on subspaces using nuclear norm as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the s...

In this paper an approach is presented how self-regulated learning can be supported and stimulated by visualising knowledge and competence structures in order to provide visual guidance in the learning process. In the field of adaptive systems and related research techniques of intelligent guidance have been developed, which, however, may have the disadvantage of limiting the learner. On the ot...

We present an algorithm for learning context free grammars from positive structural examples (unlabeled parse trees). The algorithm receives a parameter in the form of a finite set of structures and the class of languages learnable by the algorithm depends on this parameter. Every context free language belongs to many such learnable classes. A second part of the algorithm is then used to determ...

This paper discusses a data-driven learning (DDL) tool, which consists of a learner corpus for L2 learners of German. The learner corpus, in addition to submissions from ongoing current users, has been constructed from millions of submissions from a variety of activity types of approximately 5000 learners who used the E-Tutor CALL system over a period of five years. By following a cyclical proc...

An active learner is given a hypothesis class, a large set of unlabeled examples and the ability to interactively query labels to an oracle of a subset of these examples; the goal of the learner is to learn a hypothesis in the class that fits the data well by making as few label queries as possible. This work addresses active learning with labels obtained from strong and weak labelers, where in...