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CRF is a representative sequence labeling algorithm, which is a discriminative undirected probabilistic graphical model and is suitable for NER tasks.

In theory, CRFs is a representative sequence labeling algorithm, which is suitable for the NER problem.

Sequence labeling: There is a somewhat arbitrary line between the preceding task and sequence labeling.

An important unsupervised method of discovering HMM models for sequence labeling is the forward-backward (or Baum-Welch) algorithm.

Our sequence labelling approach was realised as an application of the machine learning-based conditional random fields algorithm (CRFs).

The basic principle is that the algorithms can identify time intervals in the state sequences fulfilling criteria expressed by the state sequence labels.

First, we propose an initial labeling algorithm, called ILPA, combining K-nearest neighbor (KNN) and label propagation algorithm (LPA).

We propose a heuristic labeling order and devise a parallel labeling algorithm to efficiently crowdsource the pairs following the order.

Our fusion method collects the activity labels over frames and cameras, and then fuses activity judgments as the sequence label.

Traditional BIO labels are used for the sequence labelling.

Pattern labels are formulated using the canonical labeling algorithm described in the previous section.

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