Abstract: Neural networks are currently popular learning models to represent and analyze data. We address two issues about that in this paper.

The proposed model has the advantages of applying fuzzy set theory and multi-objective sparse feature learning respectively to neural networks. Our model is an ...

For that, it is necessary to make the parameters fuzzy. In this paper, we introduce the fuzzy set theory to neural networks where the parameters are expressed ...

We propose a multi-objective optimization approach for self-organizing limited-area sparse span array, termed MOSSA.

This paper proposes a multiobjective sparse feature learning model based on the autoencoder that can learn useful sparse features and designs a multi ...

Feature selection in which most informative variables are selected for model generation is an important step in pattern recognition.

Oct 22, 2024 · Ensemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning ...

This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue.

Multi-Objective Feature Extraction. W. A. Albukhanajer, J. A. Briffa and Y. Jin. Evolutionary multi-objective image feature extraction in the presence of noise.

Oct 22, 2024 · In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by ...