Online Sequential Extreme Learning Machines: Features Combined From Hundreds of Midlayers

TL;DR

This paper introduces a hierarchical online sequential learning algorithm for neural networks that combines features from hundreds of midlayers, enhancing inference accuracy and learning speed through a structured, multi-layered approach.

Contribution

It proposes a novel hierarchical model framework with an online sequential learning algorithm that leverages features from multiple layers for improved performance.

Findings

Enhanced inference accuracy due to diverse neuron selectivity.

Faster learning speed with a wider, shallow network architecture.

Effective removal of irrelevant features through hierarchical subspace extraction.

Abstract

In this paper, we develop an algorithm called hierarchal online sequential learning algorithm (H-OS-ELM) for single feed feedforward network with features combined from hundreds of midlayers, the algorithm can learn chunk by chunk with fixed or varying block size, we believe that the diverse selectivity of neurons in top layers which consists of encoded distributed information produced by the other neurons offers better computational advantage over inference accuracy. Thus this paper proposes a Hierarchical model framework combined with Online-Sequential learning algorithm, Firstly the model consists of subspace feature extractor which consists of subnetwork neuron, using the sub-features which is result of the feature extractor in first layer of the hierarchy we get rid of irrelevant factors which are of no use for the learning and iterate this process so that to recast the the subfeatures into the hierarchical model to be processed into more acceptable cognition. Secondly by using OS-Elm we are using non-iterative style for learning we are implementing a network which is wider and shallow which plays a important role in generalizing the overall performance which in turn boosts up the learning speed