scholarly journals On inequalities of Kolmogorov type for classes of functions that are multiply monotonic on a finite interval

2021 ◽  
Vol 18 ◽  
pp. 145
Author(s):  
D.S. Skorokhodov

We solve the problem about exact constants in additive Kolmogorov-type inequalities on the class of multiply monotonic functions, defined on a finite interval.


1947 ◽  
Vol 43 (3) ◽  
pp. 289-306 ◽  
Author(s):  
Sheila M. Edmonds

The Parseval formulae for Fourier cosine and sine transforms,are of course most widely known in connexion with the classical theorems of Plancherel on functions of the class L2 (whose transforms are defined by mean convergence), and with their generalizations. We cannot expect to obtain anything as elegant as the ‘L2’ results when we consider (1) for functions of other kinds. Nevertheless, since the most obvious way of defining Fourier transforms is by means of Lebesgue or Cauchy integrals, we naturally wish to know how far the formulae (1) hold good for transforms obtained in this way. The two most familiar classes of functions having such transforms are:(i) functions f(t) integrable in the Lebesgue sense in (0, ∞), whose transforms Fe(x) and Fs(x) are defined by the Lebesgue integrals respectively; and(ii) functions f(t) which decrease in (0, ∞), tend to zero as t → ∞, and are integrable over any finite interval (0, T); in this case the transforms are defined by the Cauchy integrals .





2016 ◽  
Vol 24 ◽  
pp. 17
Author(s):  
A.Ye. Haidabura ◽  
V.A. Kofanov

We prove the equivalence theorem for additive inequalities on a finite interval. Besides, we describe a pair of constants so that the additive inequalities with that constants are valid on the whole class of functions $L_s$.



2021 ◽  
Vol 17 ◽  
pp. 120
Author(s):  
D.S. Skorokhodov

We solve the Landau-Kolmogorov problem and the Kolmogorov problem for three positive numbers on the class of functions which are absolutely monotonic on a finite interval.



2013 ◽  
Vol 56 (1) ◽  
Author(s):  
Vladimir E. Zakharov ◽  
Vyacheslav I. Karas'


1909 ◽  
Vol 16 (1) ◽  
pp. 4-9
Author(s):  
Arthur R. Schweitzer




2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.



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