Sentence examples for examples is generated from inspiring English sources

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.

After inferring labels, a set of annotated examples is generated by associating high dimensional temporal data to one dimensional target labels inferred from time series of interest, begin{aligned} forall x_i in X, x_i rightarrow l_i, D = left{ left( x_{1},l_{1} right), left( x_{2},l_{2} right),ldots,left( x_{N-Delta r},;l_{N-Delta r} right) right}.

This second set of examples is generated by a user manually placing the ASM template within the desired structure in the x-ray image, and then running the ASM algorithm.

While Grice's examples were generated lexically, other conventional implicatures are generated syntactically (see Potts 2005; 2005: 668).

Some original examples were generated and solved for the literature as well.

Some examples are generated randomly to illustrate the performance and the effectiveness of the proposed algorithms.

Two different examples were generated using different amplitude ratios of the components.

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.