In this paper, we conduct an investigation to the challenge that is training a categorization task on information represented as regards the proximity of their pairwise. The representation is not a reference point to an explicit characteristic data item representation, which makes it a more generalist approach compared to the Euclidean attribute vectors. From here, the pairwise can often be realized and computed. The first approach we put into use is based on an amalgamated linear classification and embedding process that culminates in an extension. In this extension, the Optimal Hyperplane algorithm reaches the quasi-Euclidean information. Alternatively, we put forward a second approach, one that is based upon the linear environment design in the proximity values. Third, thereafter, is optimized with the aid of Structural Risk Minimization. With our demonstration, previous knowledge or understanding of the challenge can be inculcated via the choice of measures and by assessing the various metrics via generalization. Lastly, the algorithms are optimally implemented to the protein framework data, while also being applied to data from a feline’s cerebral cortex in our experiments, they exhibited more substantial performance compared to the classification method known as K-nearest-neighbor.
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