this paper investigates learner-initiated responses to english language teachers’ referential questions and learner initiatives after teachers’ feedback moves in meaning-focused question-answer sequences to analyze how interactional practices of language teachers, their initiation and feedback moves, facilitate learner initiatives. classroom discourse research has largely neglected learner init...

The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to complex dynamic characteristics projectiles, there is an overfitting risk when single extreme learning machine (ELM) used identify parameters projectile, and identification results oscillate transonic region. To obtain accurately, parameter model based on ensemble theory ELM optimized by improved p...

Purpose The purpose of this study is to evaluate the effect learner–learner and learner–facilitator interactions on learner satisfaction their substitutability. Design/methodology/approach A quantitative survey research focusing 130 students was used collect data. Stratified sampling preferred for study, with a Likert type instrument being administered online. Findings Learner–learner mediate e...

We study Schapire's Boosting Algorithm(SBA) for use in practice. SBA is analyzed in terms of its representation and its search. We show that the SBA representation is a piecewise tiling of the domain and that if the weak learner has low coverage ability, SBA's search may fail to boost or may give a sub-optimal solution. We present a rejection boosting algorithm that trades-oo exploration and ex...

This paper introduces AdaBoost Dynamic, an extension of AdaBoost.M1 algorithm by Freund and Shapire. In this extension we use different “weak” classifiers in subsequent iterations of the algorithm, instead of AdaBoost’s fixed base classifier. The algorithm is tested with various datasets from UCI database, and results show that the algorithm performs equally well as AdaBoost with the best possi...

Numerous data mining problems involve an investigation of associations between features in heterogeneous datasets, where different prediction models can be more suitable for different regions. We propose a technique of boosting localized weak learners; rather than having constant weights attached to each learner (as in standard boosting approaches), we allow weights to be functions over the inp...

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that...