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== Model Architecture ==  
== Model Architecture ==
== ILSVRC 2014 Challenge Results ==
== ILSVRC 2014 Challenge Results ==

Revision as of 23:41, 8 November 2018

Presented by

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


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.


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


ILSVRC 2014 Challenge Results




  • [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.