Linear classification of objects using normal hyperplanes
Is devoted to the actual problem of constructing classifiers objects given by a point in a multidimensional space of feature values. A version of the geometric separation of sets by hyperplanes normal to the center-distance data sets. This approach to separating planes reduces the computational operations performed. This author separability criterion allows a normal quite effective in terms of computational complexity the exact solution of the normal separation, which requires only a linear search of points separated sets. Proposed in the article the approach to classification of sets in the multidimensional space of values ​​of their attributes can be used as a starting point for building effective in terms of computational complexity classification not only for normally separable sets, but also for more complex variations thereof. This is the most significant practical importance of materials submitted by the authors.