Источники финансирования деятельности некоммерческих организаций: понятие, классификация, особенности формирования и использования
Importance. Variety of forms of non-profit organizations, the strict legal requirement for purposes of the data according to business entities with their statutory goals causes difficulty in identifying possible to form and use the types of funding sources. Thus, the object of study in this article are the types of sources of funding for non-profit organizations prescribed by law as non-profit organizations.
Objective. The purpose of this paper is to systematize the types of sources financing of non-profit organizations on the existing forms of non-profit organizations at the legislative level. The main objectives are:
- The study of the possible types of sources of funding for nonprofit-profit organizations under civil, accounting and tax legislator-favored;
- Clarification of the conceptual apparatus of the study area;
- Grouping types of sources of funding NGOs in their fore-mothers.
Methods. The research methodology in this article is represented by such scientific methods of cognition as: analysis, synthesis, classification, logical method.
Results. The work was the preparation of, firstly, the author's classification of target financing of non-profit organizations according to the civil and accounting legislation, and secondly, the author's classification of sources of funding for non-profit organizations under the direction of their use, and thirdly, managing sources Financing for non-profit organizations in the context of statutory forms submitted by non-profit organizations;
Conclusions and Relevance. The proposed results of this article have practical value in the activities of non-profit organizations of any kind. Under the terms of the charter and the additional activities of NGOs, knowing the right to the options for the trust fund, and it is also true classifying sources of funding, non-profit organizations can avoid a number of problems in reporting for internal and external users.
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.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
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.
We propose extensions of the classical JSM-method andtheNa ̈ıveBayesianclassifierforthecaseoftriadicrelational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also evaluated the time performance of the proposed methods.
This volume is the first of its kind to offer a detailed, monographic treatment of Semitic genealogical classification. The introduction describes the author's methodological framework and surveys the history of the subgrouping discussion in Semitic linguistics, and the first chapter provides a detailed description of the proto-Semitic basic vocabulary. Each of its seven main chapters deals with one of the key issues of the Semitic subgrouping debate: the East/West dichotomy, the Central Semitic hypothesis, the North West Semitic subgroup, the Canaanite affiliation of Ugaritic, the historical unity of Aramaic, and the diagnostic features of Ethiopian Semitic and of Modern South Arabian. The book aims at a balanced account of all evidence pertinent to the subgrouping discussion, but its main focus is on the diagnostic lexical features, heavily neglected in the majority of earlier studies dealing with this subject. The author tries to assess the subgrouping potential of the vocabulary using various methods of its diachronic stratification. The hundreds of etymological comparisons given throughout the book can be conveniently accessed through detailed lexical indices.