[PERSON]. [PERSON], [PERSON], [PERSON], etc. Prominent foreign geophysicists, seismologists, geologists, and mathematicians took an active part in developing EPA. These include [PERSON] and [PERSON], members of the United States National Academy of Sciences; Professors [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], French scientists; as well as [PERSON] and [PERSON], members of the National Academy of Sciences of Italy, etc. [5; 6; 7]. In the classical Gelfand-Keilis-Borok setting, the problem of strong earthquake-prone areas recognition (EPA problem) is formulated as follows. In a considered seismically active region, it is necessary to recognize the areas prone to strong earthquakes (with magnitude \(M\geq M_{0}\), where \(M_{0}\) is a given threshold). These areas are sought among the recognition objects identified in the region. As the recognition objects, morphobstructural nodes or intersections of morphobstructural lineaments obtained as a result of morphobstructural zoning (MSZ) of the region are considered [8; 9; 10; 11; 12]. It is necessary to divide the set of recognition objects \(W\) into two non-intersecting classes: class \(B\) consisting of the objects whose vicinities are prone to strong earthquakes and class \(H\), which is composed of the objects where such earthquakes cannot occur. This classification is carried out with the employment of the pattern recognition algorithm with learning. It uses the learning set \(W_{0}\), which is determined based on the information about the seismicity of the region. In turn, \(W_{0}\) consists of two non-intersecting subsets \(B_{0}\), containing objects that are a priori referred to as class \(B\), and \(H_{0}\), containing the representatives of class \(H\). The result of applying the recognition algorithm is a decision function based on which an object from \(W\) can be attributed to class \(B\) or \(H\), and the classification of the objects itself [4; 5; 6]. A detailed literary review for almost half a century of the development and application of pattern recognition algorithms to solve the recognition problem of areas prone to strong earthquakes (EPA approach), carried out by the authors of this article, is given in [5]. This paper examines the applied pattern recognition algorithms, the studied regions, and methods for assessing the reliability of the results obtained, including the theory of dynamic and limit recognition problems. This work is devoted to the presentation of modern mathematical methods developed at the Geophysical Center of the Russian Academy of Sciences, aimed at overcoming the difficulties that may arise today when using the EPA approach in its classic 50-year version. In the present paper, the problem of earthquake-prone areas recognition is considered in the classical formulation of Gelfand-Keilis-Borok. Talking about the drawbacks of the EPA method, the following should be noted. The formation of learning material is a fundamental phase of recognition. Learning set \(B_{0}\) includes the objects with known epicenters of strong earthquakes in the vicinities. It is reasonable to assume that set \(B_{0}\) formed in this way is highly likely to contain no a priori errors or so few of them that they are unable to affect significantly the recognition result. It is hard to form a similar \"clean\" learning material of class \(H\). The set \(H_{0}\) contains either all objects not included in \(B_{0}\) or objects with known earthquakes with magnitude \(M<M_{0}-\delta\), where \(\delta>0\)[5; 13] in the vicinities. As regards the essence of the EPA problem, which represents a threshold problem of recognition [4; 14; 15], learning class \(H_{0}\) entails inherent potential errors. Accordingly, a low seismicity learning class is not at all a totality of benchmark objects that cannot be related to strong earthquake-prone areas. Learning sets \(B_{0}\) and \(H_{0}\) turn out to be disparate [16; 17], and the EPA procedure ignores this fact [13].