Multilayer extreme learning machine: a systematic review
Majority of the learning algorithms used for the training of feedforward neural networks
(FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the …
(FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the …
Multi-criteria decision-making method based on distance measure and Choquet integral for linguistic Z-numbers
Z-numbers are a new concept considering both the description of cognitive information and
the reliability of information. Linguistic terms are useful tools to adequately and effectively …
the reliability of information. Linguistic terms are useful tools to adequately and effectively …
Improving performance: A collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice
Although multi-sensor system can obtain the comprehensive information of detected object
from different information sources, the direct fusion of multi-data contains a lot of redundant …
from different information sources, the direct fusion of multi-data contains a lot of redundant …
A weighted discriminative extreme learning machine design for lung cancer detection by an electronic nose system
This article presents a study on lung cancer detection based on electronic nose technology.
The pattern recognition algorithm is extremely crucial for an electronic nose system, but the …
The pattern recognition algorithm is extremely crucial for an electronic nose system, but the …
Unsupervised feature selection based extreme learning machine for clustering
For data with various complicated distribution in the original feature space, it is difficult to find
the clusters of the data. Extreme learning machine (ELM) is famous for its universal …
the clusters of the data. Extreme learning machine (ELM) is famous for its universal …
Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis
Random vector functional link neural network (RVFLNN) is an effective and powerful neural
network model, and it has been commonly used for various engineering applications. In this …
network model, and it has been commonly used for various engineering applications. In this …
A novel learning strategy for the trade-off between accuracy and computational cost: a touch modalities classification case study
Wearable systems require resource-constrained embedded devices for the elaboration of
the sensed data. These devices have to host energy-efficient artificial intelligence (AI) …
the sensed data. These devices have to host energy-efficient artificial intelligence (AI) …
Learning structurally incoherent background and target dictionaries for hyperspectral target detection
Existing sparsity-based hyperspectral image (HSI) target detection methods have two key
problems. 1) The background dictionary is locally constructed by the pixels between the …
problems. 1) The background dictionary is locally constructed by the pixels between the …
Reducing and stretching deep convolutional activation features for accurate image classification
In order to extract effective representations of data using deep learning models, deep
convolutional activation feature (DeCAF) is usually considered. However, since the deep …
convolutional activation feature (DeCAF) is usually considered. However, since the deep …
An enhanced group recommender system by exploiting preference relation
With ties among people have been much more closer, making recommendations for groups
of users became a more general demand, which facilitates the prevalence of group …
of users became a more general demand, which facilitates the prevalence of group …