Построение диагностических моделей для бинарных данных на основе отрицательного отбора

Проведен анализ методов отрицательного отбора на основе искусственных иммунных систем, пригодных для построения диагностических моделей, работающих с бинарными данными. Проанализированы бинарные метрики, используемые в отрицательном отборе. Предложена модификация метода отрицательного отбора с цензурированием, позволяющая повысить скорость генерации набора детекторов и обеспечивающая при этом высокую точность диагностирования.Проведено аналіз методів негативного відбору на основі штучних імунних систем, що придатні для побудови діагностичних моделей, які працюють з бінарними даними. Проаналізовано бінарні метрики, що використовуються в негативному відборі. Запропоновано модифікацію методу негативного відбору із цензуруванням, яка дозволяє підвищити швидкість генерації набору детекторів і забезпечує при цьому високу точність діагностування.Negative selection methods suitable for the synthesis of diagnostic models for binary data are analyzed. Binary matching rules used in the negative selection are investigated. A modified negative selection method with censoring is proposed. It allows to increase detector generation speed and provide high accuracy of the diagnostics

Are there new models of computation? Reply to Wegner and Eberbach

Wegner and Eberbach[Weg04b] have argued that there are fundamental limitations to Turing Machines as a foundation of computability and that these can be overcome by so-called superTuring models such as interaction machines, the [pi]calculus and the $-calculus. In this paper we contest Weger and Eberbach claims

Classifiers for modeling of mineral potential

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits. Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought