Наголошено, що оновлюваність фондів є одним з важливих аргументів в обгрунтуванні витрат фінансових коштів на комплектування бібліотечних фондів. Але у підходах до оцінки цього явища та визначення показника оновлюваності фонду немає єдиної думки. Проаналізовано існуючі форми визначення показника оновлюваності фонду, запропоновано методику його розрахунку.

Подчеркнуто, что обновляемость фондов является одним из важных аргументов в обосновании финансовых средств на комплектование библиотечных фондов. Однако в подходах к оценке этого явления, а также определения показателя обновляемости фонда нет единого мнения. В проанализированы существующие формы определения показателя обновляемости фонда, предложена методика его расчета.

The necessity for collection update is one of the most significant arguments in grounding the financial assets needed for collection development. However, there is still the lack for a unified approach and single definition of this coefficient. The author analyzes the existing definitions and proposes the method to calculate the coefficient.

Currently, the Internet has given people the opportunity to access to human knowledge quickly and conveniently through various channels such as Web pages, social networks, digital libraries, portals. However, with the process of exchanging and updating information quickly, the volume of information stored (in the form of digital documents) is increasing rapidly. Therefore, we are facing challenges in representing, storing, sorting and classifying documents. In this paper, we present a new approach to text classification. This approach is based on semi-supervised machine learning and Support Vector Machine (SVM). The new point of the study is that instead of calculating the distance between the vectors by Euclidean distance, we use geodesic distance. To do this, the text must first be expressed as an n-dimensional vector. In the n-dimensional vector space, each vector is represented by one point; use geodesic distance to calculate the distance from a point to nearby points and connect into a graph. The classification is based on calculating the shortest path between vertices on the graph through a kernel function. We conducted experiments on articles taken from Reuters on 5 different topics. To evaluate the proposed Euclidean distance and the method we proposed based on geodesic distance. The experiment was performed on the same data set of 5 topics: Business, Markets, World, Politics, and Technology. The results showed that the correct classification rate is better than the traditional SVM method based on Euclidean distance (average of 3,2 %).