DIAGaRa: An Incremental Algorithm for Inferring Implicative Rules from Examples

An approach is proposed for inferring implicative logical rules from examples. The concept of a good diagnostic test for a given set of positive examples lies in the basis of this approach. The process of inferring good diagnostic tests is considered as a process of inductive common sense reasoning. The incremental approach to learning algorithms is implemented in an algorithm DIAGaRa for inferring implicative rules from examples

An Approach to Incremental Learning Good Classification Tests

An algorithm of incremental mining implicative logical rules is pro-posed. This algorithm is based on constructing good classification tests. The in-cremental approach to constructing these rules allows revealing the interde-pendence between two fundamental components of human thinking: pattern recognition and knowledge acquisition

J. Piaget's theory of intelligence: operational aspect

The Piaget's theory of intelligence is considered from the point of view of genesis and gradual development of human thinking operations. Attention is given to operational aspects of cognitive structures and knowledge. The significance of the Piaget's theory of intelligence is revealed for modeling conceptual reasoning in the framework of artificial intelligence

International Journal "Information Theories & Applications " Vol.12 DIAGARA: AN INCREMENTAL ALGORITHM FOR INFERRING IMPLICATIVE RULES FROM EXAMPLES

Abstract: An approach is proposed for inferring implicative logical rules from examples. The concept of a good diagnostic test for a given set of positive examples lies in the basis of this approach. The process of inferring good diagnostic tests is considered as a process of inductive common sense reasoning. The incremental approach to learning algorithms is implemented in an algorithm DIAGaRa for inferring implicative rules from examples

Нейроподобная комбинаторная структура данных для алгоритмов символьного машинного обучения

A new neural network-like combinatorial data-knowledge structure supporting symbolic machine learning algorithms is advanced. This structure can drastically increase the efficiency of inferring functional and implicative dependencies as like as association rules from a given dataset. Предложена новая нейроподобная комбинаторная структура данных и знаний, увеличивающая эффективность алгоритмов символьного машинного обучения для вывода различного рода логических правил из данных, таких как импликативные и функциональные зависимости, ассоциативные правила, паттерны, описывающие классы объектов. Все перечисленные зависимости генерируются с помощью одного и того же алгоритма и одной и той же предложенной структуры данных. Данная структура также интегрирует задачи вывода правил и их использования при распознавании образов

An Approach to Incremental Learning Good Classification Tests

An algorithm of incremental mining implicative logical rules is pro-posed. This algorithm is based on constructing good classification tests. The in-cremental approach to constructing these rules allows revealing the interde-pendence between two fundamental components of human thinking: pattern recognition and knowledge acquisition

An Approach to Incremental Learning Good Classification Tests

An algorithm of incremental mining implicative logical rules is pro-posed. This algorithm is based on constructing good classification tests. The in-cremental approach to constructing these rules allows revealing the interde-pendence between two fundamental components of human thinking: pattern recognition and knowledge acquisition