has been proved to be very effective in feature extraction and representation of images. For image classification problems, this work aim at finding which classifier is more competitive based on high-level deep features of images. In this report, we have discussed the nearest neighbor, support vector machines and extreme learning machines for image classification under deep convolutional activa...

Error Minimized Extreme Learning Machine (EM-ELM) proposed by Feng et al. [1] can automatically determine the number of hidden nodes in generalized Single-hidden Layer Feedforward Networks (SLFNs). We recently found that some of the hidden nodes that are added into the network may play a very minor role in the network output, which increases the network complexity. Hence, this paper proposes an...

This paper delivers a study on the change of rank of input matrix in Extreme Learning Machine (ELM) and the relationship between the rank of input matrix and the residence error of training an ELM. From the viewpoint of data analysis, the study reveals why ELM has a decreasing residence error with the increase of number of nodes in hidden layer and what role the Sigmoid function plays in increa...

This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen ...

Extreme learning machine (ELM) as an emergent technology has shown its good performance in classification applications. However, ELM algorithm needs to find the inversion of matrix in nature, which will limit its application on many occasions. This paper proposes an ELM speedup algorithm based on the analysis of ELM algorithm. By applying randomized approximation method, the proposed algorithm ...

Important brain parts like hippocampal usually being manually segmented by doctors. But with the introduction of hybrid between machine learning along with neuroimaging technique, it has proved to shows some promising results regarding on segmenting subcortical structures. However, it is known that Extreme Learning Machine (ELM) is to be superior machine learning technique. This study will inve...

Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce aliasing white noise interference; extreme learning machines (ELM) predict non-stationary data quickly easily; fruit fly optimization algorithm (FFOA) has...

Extreme Learning Machine is a fast single layer feed forward neural network for real valued classification. It suffers from the problem of instability and over fitting. Voting based Extreme Learning Machine, VELM reduces this performance variation in Extreme Learning Machine by employing majority voting based ensembling technique. VELM improves the performance of ELM at the cost of increased re...

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which coul...

HTM-MAT is a MATLAB® based toolbox for implementing cortical learning algorithms (CLA) including related cortical-like algorithms that possesses spatiotemporal properties. CLA is a suite of predictive machine learning algorithms developed by Numenta Inc. and is based on the hierarchical temporal memory (HTM). This paper presents an implementation of HTM-MAT with several illustrative examples in...