BACKGROUND Healthy young individuals benefit from sleep to promote offline enhancement of a variety of explicitly learned discrete motor tasks. It remains unknown if sleep will promote learning of other types of explicit tasks. The purpose of this study is to verify the role of sleep in learning an explicitly instructed discrete motor task and to determine if participants who practice an explic...

This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm. We show that, although the algorithm does not involve an explicit RKHS regularization term, choosing the step sizes appropriately c...

We use asymptotically optimal adaptive numerical methods (here specifically a wavelet scheme) within the offline phase of the Reduced Basis Method (RBM). The resulting parameter-dependent discretizations do not permit the standard RB “truth space”, but allow for error estimation of the RB approximation with respect to the exact solution of the considered parameterized partial differential equat...

We analyze online gradient descent learning from finite training sets at noninfinitesimal learning rates eta. Exact results are obtained for the time-dependent generalization error of a simple model system: a linear network with a large number of weights N, trained on p = alphaN examples. This allows us to study in detail the effects of finite training set size alpha on, for example, the optima...

Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical framework for planning under uncertainty. However, most real world systems are modelled by huge POMDPs that cannot be solved due to their high complexity. To palliate to this difficulty, we propose combining existing offline approaches with an online search process, called AEMS, that can improve locally an appro...

W e consider firms that feature their products on the Internet but take orders offline. Click and order data are disjoint on such non-transactional websites, and their matching is error-prone. Yet, their time separation may allow the firm to react and improve its tactical planning. We introduce a dynamic decision support model that augments the classic inventory planning model with additional c...

The paper studies the establishment of offline fingerprint library based on RSSI (Received Signal Strength Indication), and proposes WF-SKL algorithm by introducing the correlation between RSSIs. The correlations can be transformed as AP fingerprint sequence to build the offline fingerprint library. To eliminate the positioning error caused by instable RSSI value, WF-SKL can filter the noise AP...

In this paper, we study online heterogeneous transfer learning (HTL) problems where offline labeled data from a source domain is transferred to enhance the online classification performance in a target domain. The main idea of our proposed algorithm is to build an offline classifier based on heterogeneous similarity constructed by using labeled data from a source domain and unlabeled co-occurre...

OBJECTIVE To investigate whether error-related potentials can be used to increase information transfer rate of a P3 brain-computer interface (BCI) in healthy and motor-impaired individuals. METHODS Extraction and classification of the error-related potential was performed offline on data recorded from six amyotrophic lateral sclerosis (ALS) patients. An online study with 17 healthy and six mo...

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes ...