Development of Java-versioned extreme learning machine and its parallelism using MapReduce

Parallel computing is regarded as the trend in today’s data processing area. Through the idea of parallelism, people are seeking for powerful tools that can handle larger data amount in faster speed and higher precision. This project is dedicated to explore possibilities in performance enhancement o...

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Main Author: Deng, Yuchen
Other Authors: Huang Guangbin
Format: Final Year Project
Language:English
Published: 2012
Subjects:
Online Access:http://hdl.handle.net/10356/49672
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-496722023-07-07T16:03:17Z Development of Java-versioned extreme learning machine and its parallelism using MapReduce Deng, Yuchen Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Parallel computing is regarded as the trend in today’s data processing area. Through the idea of parallelism, people are seeking for powerful tools that can handle larger data amount in faster speed and higher precision. This project is dedicated to explore possibilities in performance enhancement of the enabling technology of Extreme Learning Machine by combining the idea of parallel computing. MapReduce, the programming model of Cloud Computing written in Java and originally proposed by Google Inc, is chosen to be deployed. Due to intellectual property issue, open sourced MapReduce model, by the name of Apache Hadoop MapReduce, is used in our project. In light of the nature of MapReduce which is written in Java, conventional Extreme Learning Machine is firstly developed in Java and then part of the computation is further paralleled using MapReduce. Performance of Java-versioned Extreme Learning Machine is tested and benchmarked with existing experimental data of its MatLab version. Pseudo distributed Hadoop MapReduce framework is setup and replaces the matrix multiplication portion of Extreme Learning Machine. Unfortunately, due to compatibility issue, this part of the code can’t be successfully executed, leaving the performance untested. Development and installation processes are thoroughly explained with source code attached in appendix. Bachelor of Engineering 2012-05-23T03:01:29Z 2012-05-23T03:01:29Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/49672 en Nanyang Technological University 101 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Deng, Yuchen
Development of Java-versioned extreme learning machine and its parallelism using MapReduce
description Parallel computing is regarded as the trend in today’s data processing area. Through the idea of parallelism, people are seeking for powerful tools that can handle larger data amount in faster speed and higher precision. This project is dedicated to explore possibilities in performance enhancement of the enabling technology of Extreme Learning Machine by combining the idea of parallel computing. MapReduce, the programming model of Cloud Computing written in Java and originally proposed by Google Inc, is chosen to be deployed. Due to intellectual property issue, open sourced MapReduce model, by the name of Apache Hadoop MapReduce, is used in our project. In light of the nature of MapReduce which is written in Java, conventional Extreme Learning Machine is firstly developed in Java and then part of the computation is further paralleled using MapReduce. Performance of Java-versioned Extreme Learning Machine is tested and benchmarked with existing experimental data of its MatLab version. Pseudo distributed Hadoop MapReduce framework is setup and replaces the matrix multiplication portion of Extreme Learning Machine. Unfortunately, due to compatibility issue, this part of the code can’t be successfully executed, leaving the performance untested. Development and installation processes are thoroughly explained with source code attached in appendix.
author2 Huang Guangbin
author_facet Huang Guangbin
Deng, Yuchen
format Final Year Project
author Deng, Yuchen
author_sort Deng, Yuchen
title Development of Java-versioned extreme learning machine and its parallelism using MapReduce
title_short Development of Java-versioned extreme learning machine and its parallelism using MapReduce
title_full Development of Java-versioned extreme learning machine and its parallelism using MapReduce
title_fullStr Development of Java-versioned extreme learning machine and its parallelism using MapReduce
title_full_unstemmed Development of Java-versioned extreme learning machine and its parallelism using MapReduce
title_sort development of java-versioned extreme learning machine and its parallelism using mapreduce
publishDate 2012
url http://hdl.handle.net/10356/49672
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