1. Главная
  2. Ресурсы
  3. Библиотечный поиск
A SELF-CONFIGURINGGENETIC ALGORITHM FORTHE AUTOMATED DESIGNOF SEMI-SUPERVISEDARTIFICIAL NEURALNETWORKS. Semenkina Maria2018 год

A SELF-CONFIGURING GENETIC ALGORITHM FOR THE AUTOMATED DESIGN OF SEMI-SUPERVISED ARTIFICIAL NEURAL NETWORKS. : научное издание
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: A SELF-CONFIGURING GENETIC ALGORITHM FOR THE AUTOMATED DESIGN OF SEMI-SUPERVISED ARTIFICIAL NEURAL NETWORKS. : научное издание / M. Semenkina. - Текст : непосредственный // International Journal on Information Technologies and Security. - 2018. - Т. 10, № 2. - P. 111-118. - ISSN 1313-8251.
Аннотация: Semi-supervised techniques based on using both labelled and unlabelled examples can be an efficient tool for solving real-world problems with large amounts of unlabelled data. The usual method of solving such problems demands the long work of human experts in its initial stages to prepare the learning data, a process which includes such complex tasks as the labelling of large numbers of examples. In this case, it is not necessary to label all of these many examples, but just a few of them. In this paper, a new self-configuring genetic algorithm (SelfCGA) for the automated design of semi-supervised artificial neural networks (ANN) is presented. Firstly, the performance and behaviour of the proposed semi-supervised ANNs were studied under common experimental settings and their workability was established. Then their efficiency was evaluated on a real-world problem.
Год издания: 2018
Авторы: Semenkina Maria
Издательство: Sofia
Источник: International Journal on Information Technologies and Security
Выпуск: Т. 10, № 2
Номера страниц: 111-118
Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

Похожие публикации

Semi-supervised datamining tool designwith self-tuningoptimizationtechniques Akhmedova S.Semenkina M.Stanovov V.2020 год

Semi-supervised data mining tool design with self-tuning optimization techniques

  • Akhmedova S. и др.
  • 2020 год
NonlinguisticInformationExtraction bySemi-SupervisedTechniques Semenkina M.Akhmedova Sh.Semenkin E.2017 год

Nonlinguistic Information Extraction by Semi-Supervised Techniques

  • Semenkina M. и др.
  • 2017 год
Semi-supervised SVMwith fuzzy controlledcooperation ofbiology relatedalgorithms Akhmedova S.Semenkin E.Stanovov V.2017 год

Semi-supervised SVM with fuzzy controlled cooperation of biology related algorithms

  • Akhmedova S. и др.
  • 2017 год
Semi-supervised SVMwith Fuzzy ControlledCooperation ofBiology RelatedAlgorithms Akhmedova ShakhnazSemenkin EugeneStanovov Vladimir2017 год

Semi-supervised SVM with Fuzzy Controlled Cooperation of Biology Related Algorithms

  • Akhmedova Shakhnaz и др.
  • 2017 год
Self-configuringensemble ofmultimodal geneticalgorithms Sopov E.2017 год

Self-configuring ensemble of multimodal genetic algorithms

  • Sopov E.
  • 2017 год
The parallel geneticalgorithm forconstruction oftechnological objectsneural network models Tynchenko V.S.Petrovsky E.A.Tynchenko V.V.2017 год

The parallel genetic algorithm for construction of technological objects neural network models

  • Tynchenko V.S. и др.
  • 2017 год
DISTRIBUTEDSELF-CONFIGURINGEVOLUTIONARYALGORITHMS FORARTIFICIAL NEURALNETWORKS DESIGN Khritonenko Dmitriy IvanovichSemenkin Evgeny Stanislavovich2013 год

DISTRIBUTED SELF-CONFIGURING EVOLUTIONARY ALGORITHMS FOR ARTIFICIAL NEURAL NETWORKS DESIGN

  • Khritonenko Dmitriy Ivanovich и др.
  • 2013 год

The self-assembling brain

  • Hiesinger Peter Robin
  • 2021 год

Показать ещё

INTEGRATION OFINTELLIGENTINFORMATIONTECHNOLOGIESENSEMBLES WITHSELF-CONFIGURINGGENETIC PROGRAMMINGALGORITHM Semenkin E.S.Semenkina M.E.2012 год

INTEGRATION OF INTELLIGENT INFORMATION TECHNOLOGIES ENSEMBLES WITH SELF-CONFIGURING GENETIC…

  • Semenkin E.S. и др.
  • 2012 год
Artificial NeuralNetwork ArchitectureTuning Algorithm Yurshin V. G.Stanovov V. V.2022 год

Artificial Neural Network Architecture Tuning Algorithm

  • Yurshin V. G. и др.
  • 2022 год
HYBRIDIZATION OFLOCAL SEARCH WITHSELF-CONFIGURINGGENETIC PROGRAMMINGALGORITHM FORAUTOMATED FUZZYCLASSIFIER DESIGN Semenkina M.E.2015 год

HYBRIDIZATION OF LOCAL SEARCH WITH SELF-CONFIGURING GENETIC PROGRAMMING ALGORITHM FOR AUTOMATED…

  • Semenkina M.E.
  • 2015 год
The use ofcollections ofartificial neuralnetworks to improvethe control qualityof the inductionsoldering process Milov Anton VladimirovichTynchenko Vadim SergeevichKurashkin Sergei Olegovich2021 год

The use of collections of artificial neural networks to improve the control quality of the…

  • Milov Anton Vladimirovich и др.
  • 2021 год
О ПРИМЕНЕНИИГЕНЕТИЧЕСКИХАЛГОРИТМОВ ДЛЯПОЛУАВТОМАТИЧЕСКОГООБУЧЕНИЯ Семенкина М.Е.2017 год

О ПРИМЕНЕНИИ ГЕНЕТИЧЕСКИХ АЛГОРИТМОВ ДЛЯ ПОЛУАВТОМАТИЧЕСКОГО ОБУЧЕНИЯ

  • Семенкина М.Е.
  • 2017 год
Luciferase-basedbioassay for rapidpollutants detectionand classification bymeans of multilayerartificial neural… Denisov Ivan A.2017 год

Luciferase-based bioassay for rapid pollutants detection and classification by means of…

  • Denisov Ivan A.
  • 2017 год
DEVELOPMENT OFADAPTIVE GENETICALGORITHMS FOR NEURALNETWORK MODELSMULTICRITERIA DESIGN Brester Christina YuryevnaSemenkin Evgeny Stanislavovich2013 год

DEVELOPMENT OF ADAPTIVE GENETIC ALGORITHMS FOR NEURAL NETWORK MODELS MULTICRITERIA DESIGN

  • Brester Christina Yuryevna и др.
  • 2013 год
Software forStructure Selectionof an ArtificialNeural Network toControl the InductionSoldering Process Milov A.Tynchenko V.Bukhtoyarov V.2020 год

Software for Structure Selection of an Artificial Neural Network to Control the Induction…

  • Milov A. и др.
  • 2020 год