A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing

Table 21

A comparison of handwritten digit classification results.

Referance	Methods	Accuracy %
Anupama Kaushik et al. [36]	J48	70.0
Anupama Kaushik et al. [36]	NaiveBayes	72.65
Anupama Kaushik et al. [36]	SMO	89.95
Olarik Surinta et al. [37]	Hotspot + SVM	92.70
U Ravi Babu et al. [38]	Hotspot + k-NN	96.94
Hinton GE et al. [39]	Deep Belief Network	98.75
LeCun Y et al. [40]	Deep Conv. Net LeNet-5	99.05
Wan L [41]	Deep Conv. Net (dropconnect)	99.43
Zelier MD [42]	Deep Conv. Net (stochastic pooling)	99.53
Goodfellow IJ [43]	Deep Conv. Net (maxout units and dropout)	99.55
Lee CY [44]	Deep Conv. Net (deeply-supervised)	99.61
Proposed Framework	Deep Autoencoder based on Taguchi Method	99.80