The present study examined the transfer of higher-order cognitive strategy training to inhibitory control. Middle school students enrolled in a comprehension- and reasoning-focused cognitive strategy training program and passive controls participated. The training program taught students a set of steps for inferring essential gist or themes from materials. Both before and after training or a co...

Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs. Although giving impressive empirical performance it can be slow to converge. In this paper we explore a novel strategy for training a CNN using an alternation strategy that offers substantial speedups during training. We make the following contributions: (i) replace the ReLU non-linearity within a CNN with posi...

Rats trained in a dual-solution cross-maze task, which can be solved by place and response strategies, predominantly used a response strategy after extensive training. This paper examines the involvement of the medial and lateral dorsal striatum (mDS and lDS) in the choice of these strategies after partial and extensive training. Our results show that rats with lDS and mDS lesions used mainly a...

to prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the hughes phenomenon. a practical way to handle the hughes problem is preparing a lot of training samples until the size ...

The objective of this study is to investigate the effect of metacognitive listening strategy training on the listening performance of a group of beginner preparatory school students at a university in Turkey. Two beginner groups, a control group (n: 20) and an experimental group (n: 20), were chosen as the participants of the study. The experimental group received five weeks of metacognitive st...

In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new \co-training" strategy for using un-labeled data to improve the performance of standard supervised learning algorithms. Unlike much of the prior work, such as the co-training pro...

A mixed-methods study was conducted to examine the effects of self-regulated learning (SRL) strategy training on learners’ achievement, motivation and strategy use in a web-enhanced College Success course at a community college in southeast US. It was found that training assisted with students’ overall course performance and accomplishment of long-term tasks, enhanced students’ self-satisfactio...

Cycling induced by Functional Electrical Stimulation (FES) training currently requires a manual setting of different parameters, which is a time-consuming and scarcely repeatable procedure. We proposed an automatic procedure for setting session-specific parameters optimized for hemiparetic patients. This procedure consisted of the identification of the stimulation strategy as the angular ranges...

In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new \co-training" strategy for using un-labeled data to improve the performance of standard supervised learning algorithms. Unlike much of the prior work, such as the co-training pro...

We propose and compare various sentence selection strategies for active learning for the task of detecting mentions of entities. The best strategy employs the sum of con dences of two statistical classi ers trained on di erent views of the data. Our experimental results show that, compared to the random selection strategy, this strategy reduces the amount of required labeled training data by ov...