Sentence examples for examples are generated from inspiring English sources
Some examples are generated randomly to illustrate the performance and the effectiveness of the proposed algorithms.
Various illustrative examples are generated and the semi-analytical solutions are compared against an in-house numerical code.
In particular, we present efficient algorithms for exactly identifying Boolean threshold functions and 2-term RSE, and for learning 2-term-DNF, when the examples are generated by a random walk on {0,1}n.
Some numerical examples are generated to show the performance and application of the algorithms for both Euclidean and square Euclidean distances where the MFOA has a better performance than the PSO and SA.
These examples are generated using ellipsoid uncertainty regions which we discussed in Remark 3. We now present simulation results to corroborate the result of Theorem 1 and to demonstrate the effectiveness of the SDR method.
To validate the model and show its efficiency and performance, several numerical examples are generated and solved by branch and bound (B&B) method under SOLVER of linear programming called CPLEX in the GAMS software on a personal computer (Intel Core i5-3230 M 2.6 GHz and 6 GIG) under windows 8.
Many human activities (power plants, cement plants, steel mills, and vehicles engines as a few examples) are generating high amount of waste heat at different ranges of temperature.
While Grice's examples were generated lexically, other conventional implicatures are generated syntactically (see Potts 2005; 2005: 668).
Erroneously classified objects identified in step 2 (examples that are hard to classify) are added to the training set (widehat{G}^{l}_{1}), i.e. a new training set (widehat{G}^{l}_{1mathrm{new}}) enhanced with "hard" examples is generated.
