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The criteria-based approaches try to optimize clustering fitness functions using different optimization techniques.

CS-based approaches try to estimate channels individually while DCS-based channel estimation tries to estimate jointly sparse channels altogether.

CS-based approaches try to estimate channels individually while DCS-based channel estimation tries to determine jointly sparse channels altogether.

On the other hand, texture-based approaches try to find robust local features that are invariant to pose or lighting variations.

Model-based approaches try to model a spot's intensity as a Gaussian normal distribution or some variant thereof.

Dictionary-based approaches, try to identify entity names belonging to domain-specific controlled vocabularies, taxonomies and/or ontologies directly from the literature.

Most of these approaches try to learn a linear combination of base kernels, which can be interpreted as the concatenation of the base kernel feature space or an 'OR' combination of the individual kernels.

Molecular shape or volume approaches try to maximize the overlap of shapes or volumes and determine a similarity value based on the overlap.

Structure‐based approaches try to predict the conformation and orientation of potential ligands in the binding pocket of a target protein [ 3], which requires experimentally determined three‐dimensional coordinates of the protein structure.

In the following section we extend this age-of-youngest based approach to try to better pin down the causal impact of activation on outcomes in a proportional hazard model framework.

New methods have been suggested for dealing with complex scenarios in which it is difficult or impossible to map reads uniquely, such as within recent segmental duplications, using hill climbing [ 25] or parsimony [ 26] based approaches which try to minimize the number of observed structural variants.

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