Sentence examples for examples were generated from inspiring English sources

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

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

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

Negative examples were generated from the respective precursor sequences based on the observation that known miRNA precursors do not produce multiple overlapping mature miRNAs from the same arm of the fold-back precursor [28].

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.

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.

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.

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}.