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== References ==
 
== References ==
  
* <sup>[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1380068 [1]]</sup>G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in Proc. IJCNN,
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* <sup>[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1380068 [1]]</sup>G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in Proc. IJCNN,Budapest, Hungary, Jul. 25–29, 2004, vol. 2, pp. 985–990.
Budapest, Hungary, Jul. 25–29, 2004, vol. 2, pp. 985–990.
 

Revision as of 22:45, 8 November 2018

Presented by

Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang

Introduction

In the past two decades, due to their surprising classi- fication capability, support vector machine (SVM) [1] and its variants [2]–[4] have been extensively used in classification applications. Least square support vector machine (LS-SVM) and proximal sup- port vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification appli- cations directly, although variants of LS-SVM and PSVM have been proposed to handle such cases.

Motivation

There are several issues on BP learning algorithms:

(1) When the learning rate Z is too small, the learning algorithm converges very slowly. However, when Z is too large, the algorithm becomes unstable and diverges.

(2) Another peculiarity of the error surface that impacts the performance of the BP learning algorithm is the presence of local minima [6]. It is undesirable that the learning algorithm stops at a local minima if it is located far above a global minima.

(3) Neural network may be over-trained by using BP algorithms and obtain worse generalization performance. Thus, validation and suitable stopping methods are required in the cost function minimization procedure.

(4) Gradient-based learning is very time-consuming in most applications.

Previous Work

Model Architecture

aa.png

ILSVRC 2014 Challenge Results

Conclusion

Critiques

References

  • [1]G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in Proc. IJCNN,Budapest, Hungary, Jul. 25–29, 2004, vol. 2, pp. 985–990.