Классификация историографических источников: опыт соотнесения библиографического и источниковедческого подходов
Classification of historiographical sources is an important element of source studies of historiography, which uses the method of source studies for investigating the history of historical knowledge in the context of intellectual history. The article compares the classification of historiographical sources in sourse studies and classification of publications in System of standards on information, librarianship and publishing (SIBID). Match criteria – the purpose of creation/ publication. It uses sourse studies and bibliography.
The purpose of this book is to teach students how to write extended essays in English. It is supplementary to the British course book ‘English for Academic Study: Extended Writing and Research Skills’ (Garnet Publishing Ltd.). It was designed for students , teachers and those who are interested in obtaining the skill of extended essay writing.
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.
Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on counter-examples and discuss them in terms of pattern structures. We show how such classifiers are related. In particular, we discuss the equivalence between good maximally redundant tests and minimal JSM-hyposethes and between minimal representations of version spaces and good irredundant tests.
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
The paper characterizes the first steps in research eminent specialist in archeography, source studies, research Metrica of the Grand Duchy of Lithuania.