We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
Robust model selection procedures control the undue influence that outliers can have on the selection criteria by using both robust point estimators and a bounded loss function wh...
Following the work of Hurvich, Shumway, and Tsai (1990), we propose an "improved" variant of the Akaike information criterion, AICi, for state-space model selection. The...
This work proposes a Web Service (WS) discovery model in which the functional and nonfunctional requirements are taken into account during service discovery. The proposed infrastr...
For complex tasks such as parse selection, the creation of labelled training sets can be extremely costly. Resource-efficient schemes for creating informative labelled material mu...