Artificial Intelligence: 19th Russian Conference

Автор: literator от 7-10-2021, 22:01, Коментариев: 0

Категория: КНИГИ » ПРОГРАММИРОВАНИЕ

Artificial Intelligence: 19th Russian ConferenceНазвание: Artificial Intelligence: 19th Russian Conference
Автор: Sergei M. Kovalev, Sergei O. Kuznetsov
Издательство: Springer
Год: 2021
Страниц: 381
Язык: английский
Формат: pdf (true)
Размер: 25.4 MB

This book constitutes the proceedings of the 19th Russian Conference on Artificial Intelligence, RCAI 2021, held in Moscow, Russia, in October 2021. The 19 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 80 submissions. The conference deals with a wide range of topics, categorized into the following topical headings: cognitive research; data mining, machine learning, classification; knowledge engineering; multi-agent systems and robotics; natural language processing; fuzzy models and soft computer; intelligent systems; and tools for designing intelligent systems.

Algebraic Bayesian networks and Bayesian belief networks are one of the probabilistic graphical models. One of the main tasks which need to be solved during the networks’ handling is the model structure training. This paper is dedicated to the automation of this process for algebraic Bayesian networks. This work relates to the PC-algorithm for algebraic Bayesian network secondary structure training. The algorithm is based on the PC-algorithm for Belief Bayesian networks training. The algorithm pseudocode and usage example are described. The provided algorithm helps investigate the full-automated machine learning of algebraic Bayesian networks. Earlier, the structure was provided by experts.

A new memory structure for artificial neural networks of adaptive-resonance theory is proposed, which has a hierarchical form. For each new memory level, the previous classification value is refined by increasing the similarity parameter. This architecture was used to determine the network state in intrusion detection systems. The paper describes an algorithm for learning the proposed structure of the ART-2m network in parallel mode. A comparative analysis of the time characteristics of the network with the proposed structure when operating in series and parallel modes is carried out. Experiments were carried out using the NSL KDD-2009 sample, the results of which show the possibility of using ART-2m in intrusion detection systems.

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