(iii) ELM-AE: the single-column ELM-AE was implemented on SIF features for each sound segment with the ELM classifier, and then, only the second-stage ensemble learning was used to get the final decision on all segments.
Instead, here we are offering a generic approach to continuous action formation, where reinforcement learning is implemented on the features defining continuous actions.
Our existing implementation structurally relies on an open source workflow suite; however, it can also be implemented on more feature-rich, commercial, XPDL-compliant workflow suites (e.g., Tibco business process management platform from Tibco software (Palo Alto, CA, USA) or Fujitsu Interstage platform).
The significance of the proposed model is as follows: (1) feature selection is implemented on a reduced feature space, so that the dimension of optimal feature subset is smaller; (2) the fitness function in CBPSO algorithm optimizes the predicting performance and the correlation between features and target variable.
The B-MC-ELM-AE algorithm is then implemented on the SIF features for each frame to learn more effective feature representation, which will generate multiple-channel bilinear features.
We designed a comprehensive feature selection procedure that was implemented on different sets of feature including: 1) all genes represented in the microarray; 2) set of genes significantly discriminated between groups of different classes of time to next relapse; 3) genes significantly correlated with time to next relapse, and 4) clinical and demographical confounders.
(iv) B-ELM-AE: ELM-AE was conducted twice on SIF features belonging to the same sound segment, respectively, and then, the bilinear model was implemented on these two ELM-AE features to generate bilinear features for classification with only the second-stage ELM-based ensemble learning on all segments.
To this end, we investigated user preferences of the social features implemented on current social commerce websites.
The developed control is implemented on the Zebdoard, which features a ZYNQ device combining dual-core ARM Cortex-A9 processors with traditional Field Programmable Gate Array (FPGA) logic fabric.
Since we have no resubmission features implemented on the Globus scheduler, this variation has direct relation with the model grid size of each region of Brazil.
