Sentence examples for classification of time from inspiring English sources

One of the challenging tasks in machine learning is the classification of time series.

The literature contains examples of the combining of DTW and DDTW in algorithms for supervised classification of time series data.

This classification of time has been increasingly superseded since the late 20th century by the periods before the Common Era (bce) and Common Era (ce), respectively.

Robust signatures are needed for classification of time series of satellite images to monitor land cover change.

It is not clear how to use these methods for classification of time series data.

Landscape information was obtained from the 2004 2006 ESA Globcover Land Cover layer derived from an automatic and regionally-tuned classification of time series of MERIS-FR composites.

Our experiments suggest that temporal encoding is a crucial ingredient for successful classification of time-series data.

LULC classification was based on quantitative method that requires an independent classification of time-series images for the same geographic location followed by a comparison of the corresponding pixels to identify and quantify areas of change (Lillesand and Kiefer 1994).

The obtained features are explained as follows: Moments of base and coefficient vectors (i.e., W and H, respectively) carry an important information of the TF characteristics of a signal and could be used for classification of time-varying signals [17] and feature identification [18].

The phylum-level classifications of time series originating from five pigs from experiment 1−3 are shown in Figure 1.

Our proposed CTA method offers a novel way of combining temperature and acceleration information to improve the classification of wear time and non-wear time events.