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Similarly usual dictionary-based approaches are relatively ineffective.
However, clone-based approaches are relatively time-consuming, expensive and labour-intensive when applied to the analysis of large sample sets.
Although dictionary-based approaches are relatively simple to design and implement if the appropriate dictionary is available, they have several limitations.
Another problem of Landsat based approaches is related to the impact of shadows caused by canopy in complex stand structures and relatively complex topography [27, 28].
A family based approach is imperative.
The three chosen machine learning approaches are relatively simple and are largely based on previously described algorithms.
Bussey [ 37] conducted an investigation of methods used in obedience training at a time when use of reward based training approaches were relatively new in this discipline.
Convex relaxation approaches are relatively computationally complex.
Both approaches are relatively complex.
Though not reported here some simulations based on the non-normal data show that, in general, the approaches were relatively robust to departures from multivariate normality.
As yet, empirical evidence from such approaches is relatively scarce.
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