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Found 10 papers, 0 papers with code
Extreme Learning Tree
The paper proposes a new variant of a decision tree, called an Extreme Learning Tree.
Mislabel Detection of Finnish Publication Ranks
The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM).
Per-sample Prediction Intervals for Extreme Learning Machines
Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall.
Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices
This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices.
Incremental ELMVIS for unsupervised learning
An incremental version of the ELMVIS+ method is proposed in this paper.
A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning
The results show that the best method of classifying a webpage into the studies classes is to assign the class according to the maximum probability of any image belonging to this (weapon) class being above the threshold, across all the retrieved images.
Embedded Spectral Descriptors: Learning the point-wise correspondence metric via Siamese neural networks
In this paper, it is proposed a method to improve the similarity metric of spectral descriptors for correspondence matching problems.
HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection
By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in.
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.
LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data
Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data.
