Sentence examples for sophisticated classification from inspiring English sources

The Naive Bayes can outperform more sophisticated classification methods, despite its simplicity.

To improve the diagnosis sensitivity of mediastinal lymph node NSCLC, more sophisticated classification strategy is needed and computerized machine learning algorithms could be of help.

We plan to extend this preliminary study using both more sophisticated classification schemes, such as the use of SVMs and a more comprehensive set of speech features, such as variations of our speech-rate measures, power, fundamental frequency measures, and temporal- and frequency-based rhythmic/modulation patterns.

More sophisticated classification arrangements were proposed by Pseudo-Geber (in the Geber corpus, c. 1310), Paracelsus (De Natura Rerum libri nonem, 1525 6; and later works), Basil Valentine (Conclusiones, 1624), and Boerhaave (Elementa Chemiæ, 1733).

Finally, our study shows how functional metabolomics can contribute to obtaining a more sophisticated classification of the disease as well as rational optimization of diagnostic and treatment options, as recently suggested by Bain et al. [4].

It may be possible to improve performance further by making use of a richer set of features in a more sophisticated classification framework, including, say, the primary traces for sequencing reads and the full multiple alignment of reads used by the assembler [19].

We show how the application achieves comparable accuracy to the winning team's sophisticated malware classification techniques using only a fraction of the time required to generate the Strand training model.

While journal impact factors may thus indeed provide a meaningful qualitative measure of impact, a fair quantitative comparison requires a more sophisticated journal classification system, together with multiple field-specific impact statistics per journal.

The Dutch cohort study used a more sophisticated dosage classification [ 21].

Finally, sophisticated statistical classification techniques, including but not limited to neural networks [ 29], support vector machines [ 35] and Bayesian networks [ 30], are being updated to accommodate analysis of interactions.

These initial works offered valuable insights into tumor biology and demonstrated that intrinsic gene expression patterns could be used in conjunction with histopathological characteristics for a far more sophisticated tumor classification system.