Dolphin Swarm Extreme Learning Machine View Full Text


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Article Info

DATE

2017-04

AUTHORS

Tianqi Wu, Min Yao, Jianhua Yang

ABSTRACT

As a novel learning algorithm for a single hidden-layer feedforward neural network, the extreme learning machine has attracted much research attention for its fast training speed and good generalization performances. Instead of iteratively tuning the parameters, the extreme machine can be seen as a linear optimization problem by randomly generating the input weights and hidden biases. However, the random determination of the input weights and hidden biases may bring non-optimal parameters, which have a negative impact on the final results or need more hidden nodes for the neural network. To overcome the above drawbacks caused by the non-optimal input weights and hidden biases, we propose a new hybrid learning algorithm named dolphin swarm algorithm extreme learning machine adopting the dolphin swarm algorithm to optimize the input weights and hidden biases efficiently. Each set of input weights and hidden biases is encoded into one vector, namely the dolphin. The dolphins are evaluated by root mean squared error and updated by the four pivotal phases of the dolphin swarm algorithm. Eventually, we will obtain an optimal set of input weights and hidden biases. To evaluate the effectiveness of our method, we compare the proposed algorithm with the standard extreme learning machine and three state-of-the-art methods, which are the particle swarm optimization extreme learning machine, evolutionary extreme learning machine, and self-adaptive evolutionary extreme learning machine, under 13 benchmark datasets obtained from the University of California Irvine Machine Learning Repository. The experimental results demonstrate that the proposed method can achieve superior generalization performances than all the compared algorithms. More... »

PAGES

275-284

References to SciGraph publications

  • 2014-06. Fast Face Recognition Via Sparse Coding and Extreme Learning Machine in COGNITIVE COMPUTATION
  • 2015-06. What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle in COGNITIVE COMPUTATION
  • 2014-09. A Class Incremental Extreme Learning Machine for Activity Recognition in COGNITIVE COMPUTATION
  • 2015-02. A Kernel Clustering-Based Possibilistic Fuzzy Extreme Learning Machine for Class Imbalance Learning in COGNITIVE COMPUTATION
  • 2014-09. A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data in COGNITIVE COMPUTATION
  • 2014-09. An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels in COGNITIVE COMPUTATION
  • 2015-06. Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine in COGNITIVE COMPUTATION
  • 2015-02. A Cognitive Ensemble of Extreme Learning Machines for Steganalysis Based on Risk-Sensitive Hinge Loss Function in COGNITIVE COMPUTATION
  • 2012-12. Self-Adaptive Evolutionary Extreme Learning Machine in NEURAL PROCESSING LETTERS
  • 2007. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems in FOUNDATIONS OF FUZZY LOGIC AND SOFT COMPUTING
  • 2006. Evolutionary Extreme Learning Machine – Based on Particle Swarm Optimization in ADVANCES IN NEURAL NETWORKS - ISNN 2006
  • 2014-09. Fast Image Recognition Based on Independent Component Analysis and Extreme Learning Machine in COGNITIVE COMPUTATION
  • 2014-09. Feature Component-Based Extreme Learning Machines for Finger Vein Recognition in COGNITIVE COMPUTATION
  • 2014-09. A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification in COGNITIVE COMPUTATION
  • 2015-02. Classification of Uncertain Data Streams Based on Extreme Learning Machine in COGNITIVE COMPUTATION
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    http://scigraph.springernature.com/pub.10.1007/s12559-017-9451-y

    DOI

    http://dx.doi.org/10.1007/s12559-017-9451-y

    DIMENSIONS

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