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Found 16 papers, 1 papers with code
Class-incremental Learning for Time Series: Benchmark and Evaluation
Real-world environments are inherently non-stationary, frequently introducing new classes over time.
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years.
Heterogeneous Oblique Double Random Forest
Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node.
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
The RL-GP adopts the ensemble population strategies.
Ensemble Reinforcement Learning: A Survey
By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL.
A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition
Specifically, the performance of different decomposition methods and ensemble modes was further compared.
Random vector functional link network: recent developments, applications, and future directions
To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed.
Incremental Knowledge Tracing from Multiple Schools
Knowledge tracing is the task of predicting a learner's future performance based on the history of the learner's performance.
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation
We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space.
Oblique and rotation double random forest
The oblique double random forest models are multivariate decision trees.
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Electricity load forecasting is crucial for the power systems' planning and maintenance.
Ensemble deep learning: A review
Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance.
Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification
Specifically, we introduce direct connections (feature reuse) from preceding layers to the fore layers of the network as in the original RVFL network.
Random Vector Functional Link Neural Network based Ensemble Deep Learning
The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed form solution as in a standard RVFL network.
A Many-Objective Evolutionary Algorithm Based on Decomposition and Local Dominance
Recent studies show that a well designed combination of the decomposition method and the domination method can improve the performance , i. e., convergence and diversity, of a MOEA.
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces
In this paper, we first present a new variant of oblique decision tree based on a linear classifier, then construct an ensemble classifier based on the fusion of a fast neural network, random vector functional link network and oblique decision trees.
