Cluster set construction In its while loop, the algorithm iteratively generates fixed-size cluster sets such that each data point belongs to exactly one cluster in one set.

Then, it gradually extends the clusters by iteratively mapping the samples, in decreasing order of probability, to the existing clusters (the mlMappz'ng function).

By iteratively extending the clusters with high probability subsets, we thus expect each cluster set to consist of clusters that demonstrate these properties.

17: for n <— 1...Maa: Iter do

27: forn <— 1...Maa: Iter do

36: forn <— 1...Maa: Iter do

The alignment is updated iteratively until no additional inconsistent links can be removed.

To estimate the confidence of each opinion target candidate, we employ a random walk algorithm on our graph, which iteratively computes the weighted average of opinion target confidences from neighboring vertices.

Moreover, (Qiu et al., 2011) proposed a Double Propagation method to expand sentiment words and opinion targets iteratively , where they also exploited syntactic relations between words.

for some (differentiable) function one iteratively starts at the current point {pkg computes the gradient VEi({pk(j and goes to the point

For computing alignments, we use the common procedure of hillclimbing where we start with an alignment, then iteratively compute the probabilities of all alignments differing by a move or a swap (Brown et al., 1993) and move to the best of these if it beats the current alignment.

Proper EM The expectation maximization (EM) framework (Dempster et al., 1977; Neal and Hinton, 1998) is a class of template procedures (rather than a proper algorithm) that iteratively requires solving the task