The incremental combination of adaptive filters (AFs), recently introduced in the literature, presents intrinsic features capable of improving the overall filtering performance. In this work, the incremental combination is extended to account for AFs with different adaptive rules; when Recursive Least-...
Incrementally learning new information from a non-stationary stream of data, referred to as ‘continual learning’, is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning meth
We hope for AFL ++ to become a new baseline tool not only for current, but also for future research, as it allows to test new techniques quickly, and evaluate not only the effectiveness of the single technique versus the state-of-the-art, but also in combination with other techniques. ...
Database and model queries are the foundations of data-driven applications. Their performance is of primary importance in model-driven software engineering (MDE), es- pecially with the evergrowing complexity of software modeling projects. To address the scalability issues of traditional MDE tools, di...
aa combination of methods 方法的组合 [translate] aFor pre-qualifying equipment, the time history test may be used 对于前合格的设备,时间历史测试也许使用 [translate] a上下盖子 正在翻译,请等待... [translate] awave down 下来波浪 [translate] anunca brochar 对从未提 [translate] ato make a ...
We show that a combination of moderate Client Drift and Catastrophic Forgetting can even improve the performance of the resulting model (causing a "Generalization Bump") compared to when only one of the shifts occurs individually. We apply a simple and commonly used method from Continual Learning ...
SUPPORT VECTOR REGRESSIONELECTRICITYCONSUMPTIONWAVELET TRANSFORMFUZZY-LOGICARIMA MODELSYSTEMCOMBINATIONINTELLIGENCEErratum to: Neural Comput & Applic DOI 10.1007/s005... M Sheikhan,N Mohammadi - 《Neural Computing & Applications》 被引量: 31发表: 2013年 Identification of Quasi-ARX Neurofuzzy Model with an...
validity over individual job attitudes (R2 change of 0.02 to 0.06), and (2) EE bears low incremental validity over a higher-order job attitude construct representing the combination of other job attitudes in the prediction of a higher-order employee effectiveness construct (R2 change of 0.01). ...
The normalization reduces the number of different OCL operators appearing in their body (for instance, replacing the implies operator with a combination of the not and or operators or the exists operator with a combination of the select and size operators). This representation is automatically ...
A state is taken to be a combination of the robot's sensor status. Each sensor is viewed as an independent component. The importance of each sensor status relative to each action is computed based on the frequency of its occurrences. Not all sensors are needed for every action, for example...