Maciej Jaworski
Orcid: 0000-0002-8410-4231
According to our database1,
Maciej Jaworski
authored at least 45 papers
between 2011 and 2023.
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Bibliography
2023
The <i>L</i><sub>2</sub> convergence of stream data mining algorithms based on probabilistic neural networks.
Inf. Sci., 2023
2022
A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers.
IEEE Trans. Neural Networks Learn. Syst., 2022
2021
Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines.
Proceedings of the Artificial Intelligence and Soft Computing, 2021
2020
On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With Applications to Pattern Classification.
IEEE Trans. Cybern., 2020
J. Artif. Intell. Soft Comput. Res., 2020
Proceedings of the Artificial Intelligence and Soft Computing, 2020
Proceedings of the Artificial Intelligence and Soft Computing, 2020
2019
Corrigendum to 'How to adjust an ensemble size in stream data mining?' Information Sciences, vol. 381 (2017), pp. 46-54.
Inf. Sci., 2019
On Explainable Flexible Fuzzy Recommender and Its Performance Evaluation Using the Akaike Information Criterion.
Proceedings of the Neural Information Processing - 26th International Conference, 2019
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine.
Proceedings of the Neural Information Processing - 26th International Conference, 2019
Proceedings of the Artificial Intelligence and Soft Computing, 2019
2018
IEEE Trans. Neural Networks Learn. Syst., 2018
Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks.
Inf. Sci., 2018
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.
Int. J. Neural Syst., 2018
Regression Function and Noise Variance Tracking Methods for Data Streams with Concept Drift.
Int. J. Appl. Math. Comput. Sci., 2018
Proceedings of the Advances in Neural Networks - ISNN 2018, 2018
Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018
Estimation of Probability Density Function, Differential Entropy and Other Relative Quantities for Data Streams with Concept Drift.
Proceedings of the Artificial Intelligence and Soft Computing, 2018
2017
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017
On ensemble components selection in data streams scenario with reoccurring concept-drift.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017
Heuristic Regression Function Estimation Methods for Data Streams with Concept Drift.
Proceedings of the Artificial Intelligence and Soft Computing, 2017
2016
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
Proceedings of the Artificial Intelligence and Soft Computing, 2016
On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions.
Proceedings of the Artificial Intelligence and Soft Computing, 2016
2015
IEEE Trans. Neural Networks Learn. Syst., 2015
2014
IEEE Trans. Knowl. Data Eng., 2014
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014
A novel application of Hoeffding's inequality to decision trees construction for data streams.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014
2013
IEEE Trans. Knowl. Data Eng., 2013
On a Splitting Criterion for Decision Trees in Data Streams.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2013
On the Application of Orthogonal Series Density Estimation for Image Classification Based on Feature Description.
Proceedings of the Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions - Selected Papers from KICSS'2013, 2013
Proceedings of the Artificial Intelligence and Soft Computing, 2013
2012
Proceedings of the Artificial Intelligence and Soft Computing, 2012
Proceedings of the Artificial Intelligence and Soft Computing, 2012
On Learning in a Time-Varying Environment by Using a Probabilistic Neural Network and the Recursive Least Squares Method.
Proceedings of the Artificial Intelligence and Soft Computing, 2012
On the Application of the Parzen-Type Kernel Regression Neural Network and Order Statistics for Learning in a Non-stationary Environment.
Proceedings of the Artificial Intelligence and Soft Computing, 2012
Proceedings of the Artificial Intelligence and Soft Computing, 2012
Proceedings of the Artificial Intelligence and Soft Computing, 2012
On the Strong Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Non-stationary Environment.
Proceedings of the Artificial Intelligence and Soft Computing, 2012
2011
Learning in a Time-Varying Environment by Making Use of the Stochastic Approximation and Orthogonal Series-Type Kernel Probabilistic Neural Network.
Proceedings of the Parallel Processing and Applied Mathematics, 2011
On the Application of the Parzen-Type Kernel Probabilistic Neural Network and Recursive Least Squares Method for Learning in a Time-Varying Environment.
Proceedings of the Parallel Processing and Applied Mathematics, 2011
Learning in a Non-stationary Environment Using the Recursive Least Squares Method and Orthogonal-Series Type Regression Neural Network.
Proceedings of the Parallel Processing and Applied Mathematics, 2011