In this article, we tackle the problem of speaker role detection from broadcast news shows. In the literature, many proposed solutions are based on the combination of various features coming from acoustic, lexical and semantic information with a machine learning algorithm. Many previous studies mention the use of boosting over decision stumps to combine efficiently these features. In this work,...

We emphasize here that the weak learning algorithm A does not know the distribution D. It only sees samples from D. The time and sampling complexity of A usually depends on δ and γ. The definition of weak learner is different from the normal but strong version of PAC learning in terms of the error guarantee: the strong version requires that for any ε > 0, the error PrD[f(x) 6= c(x)] can be made...

This paper presents a new and fast binary descriptor for image matching learned from Haar features. The training uses AdaBoost; the weak learner is built on response function for Haar features, instead of histogram-type features. The weak classifier is selected from a large weak feature pool. The selected features have different feature type, scale and position within the patch, having correspo...

Importance Sampled Circuit Learning Ensembles (ISCLEs) is a novel analog circuit topology synthesis method that returns designertrustworthy circuits yet can apply to a broad range of circuit design problems including novel functionality. ISCLEs uses the machine learning technique of boosting, which does importance sampling of “weak learners” to create an overall circuit ensemble. In ISCLEs, the...

Fill-in-the-blank items are commonly featured in computer-assisted language learning (CALL) systems. An item displays a sentence with a blank, and often proposes a number of choices for filling it. These choices should include one correct answer and several plausible distractors. We describe a system that, given an English corpus, automatically generates distractors to produce items for preposi...

In designing intelligent web based educational systems different student needs and preferences should be taken into consideration. Personalization of a system usually results in an increase in its effectiveness, which can be measured by the degree to which learning outcomes are achieved. However, taking into account the individual requirements of each learner and adjusting the system to their n...

Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners. However, the generalization ability of such individual learner can ...