A key area of Theaetetus's work was on incommensurables (which correspond to irrational numbers in modern mathematics), in which he extended the work of Theodorus by devising the basic classification of incommensurable magnitudes into different types that is found in Book X of the Elements.
We adopt 'local mean vector' as the basic classification model, and then through linear transformation, large margins between heterogeneous local parts are introduced.
The basic classification algorithms were based on logistic regression, LS-SVMs, and kernel logistic regression.
The screening score is calculated with the basic classification values (+1 or −1) determined by the cut-off threshold.
However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis.
Since the basic classification unit is a sentence fragment with a few words, leading to sparse representation, we use support vector machines (SVMs), which work well on sparse data and typically outperform other text classification methods.
Historically, tissue biopsies, analyzed by hematoxylin and eosin stains that are often supplemented by additional special stains, have provided information for the basic classification of disease and characterization of neoplasms.
