mapreduce based parallel neural networks in enabling large scale machine learning

Clicks: 215
ID: 221264
2015
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #26 of 77 articles by views in Organic Chemistry Frontiers

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.
Reference Key
liu2015computationalmapreduce Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yang Liu;Jie Yang;Yuan Huang;Lixiong Xu;Siguang Li;Man Qi
Journal Organic Chemistry Frontiers
Year 2015
DOI
10.1155/2015/297672
URL
http://dx.doi.org/10.1155/2015/297672
Keywords
computer applications to medicine medical informaticsneurosciences neuropsychiatry biological psychiatry

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.