Sentence examples for single set of features from inspiring English sources

However, there is no single set of features that fits all classes.

Second, we observe that there is no single set of features that correlates with signature effectiveness for all signature classes.

Whether there should be more than a single set of features to be synthesized, an individual feature synthesizer will be evolved for each FS.

Results indicated that groups of three structural parameters (among the 57 calculated) could successfully identify foam structures with different characteristics but unfortunately no single set of features could be used ubiquitously.

The crustaceans pose a particularly difficult challenge in this regard, however, as there is so much morphological diversity that it is difficult to characterize crustaceans with any single set of features, and attempts at understanding their taxonomic groups and phylogenetic relationships based on morphology have been problematic for as long as they have been studied.

However, combining both transcripts and markers into a single set of features rarely performs better than taking the best of marker-only or transcript-only prediction accuracy, possibly due to the added noise of too large a set of features.

For chosen parameter settings, the average number of unique feature elements per iris-code block, l, and the corresponding amount of different sequences of iris-code columns which result in a single set of feature values, ℓ, according to Eq. 1, is summarized in Table 4.

Suicide attempts are complex acts for which no single set of clinical features can be expected to be a good predictor [ 7].

We applied two fundamentally different machine learning methods, support vector machines (SVM) and random forests (RF) to successfully address three different classification cases: to distinguish bacteria from archaea, halophiles from non-halophiles, as well as mesophiles from thermophiles and mesothermophiles, always by using a single set of 42 features.

Despite extensive efforts, a single set of parameters that predicts all features visible in the measured data at the same time could not be found.

Therefore, we will compare recognition accuracies obtained when estimating state likelihoods using a single set of exemplars represented by 135-D feature vectors and the fusion of the state likelihoods estimated from the 135-D system and nine sets of exemplars (one for each modulation band) represented as 15-D feature vectors (for the 15 gammatone filters).