DTW is widely used, for example, for classification and clustering tasks in econometrics, chemometrics, and general time series mining (Giorgino 2015).
As the era of molecular technology for subtyping disease and identifying molecular targets takes huge leaps forward it is tempting to begin to ignore the more traditional morphological classification of disease and consider molecular subtyping (for example, luminal, basal, HER2 subtypes) and testing (for example, OncotypeDX) for classification and management.
Examples for successful classification of earlier time points are Rx99 (24 hr) and methylene dianiline (3 hr).
The implications of these examples for Phragmites classification are discussed in the concluding section.
The corpus statistics of the examples for text classification were listed in Table 1.
Support vector machines (SVMs) are computational algorithms that can learn from training examples for binary classification problems.
Some prominent examples for protein classification are SYSTERS (Liu and Rost, 2003), CluSTr (Petryszak et al., 2005) and ProtoNet (Rappoport et al., 2013).
As it can be seen in Table 2, better accuracies are related to those systems adopting different technologies, in particular when technologies are exploited to detect metrological parameters fitting the specific characteristics of the sensors (for example, laser for classification and counting, radar for speed, etc).
They can be either specifically developed in the survival analysis framework, such as the Royston-Sauerbrei D-measure [ 30] or Zheng et al. [ 31]'s positive prediction value, or adapted from different context, for example from classification problems.
