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eyes:logics:classification [2017/06/06 17:32] nfische [Concept] |
eyes:logics:classification [2017/06/12 17:27] (current) nfische [Example] |
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| A prerequisite for a feasible **classification** is a complexity reduction of the given input dataset. This can be achieved by performing a [[:eyes:logics:pca]]. The PCA can be carried out either internally by the classification-logic or beforehand by the respective PCA logic. Additionally, the user has to define the input set and the number of expected classes/clusters. Now, the logic will split the dataset according to the information provided by the PCA into as many classes/clusters as determined aiming for an optimum of i) minimal variance within each class/cluster and ii) a maximized signal-to-noise ratio. | A prerequisite for a feasible **classification** is a complexity reduction of the given input dataset. This can be achieved by performing a [[:eyes:logics:pca]]. The PCA can be carried out either internally by the classification-logic or beforehand by the respective PCA logic. Additionally, the user has to define the input set and the number of expected classes/clusters. Now, the logic will split the dataset according to the information provided by the PCA into as many classes/clusters as determined aiming for an optimum of i) minimal variance within each class/cluster and ii) a maximized signal-to-noise ratio. | ||
| - | ===== Example ==== | ||
| - | FIXME | ||
| ===== Modes ===== | ===== Modes ===== | ||