Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

Table 5

The comparisons of training and testing on the regression cases.

Datasets	Algorithms	Training accuracy ()	Training time (s)	Testing accuracy ()	Testing deviation ()
Parkinsons	SVM	95.42	282.26	95.43	0.01
	ELM	95.75	240.68	95.73	0.29
	ML-ELM	97.93	482.7	97.96	0.08
	AE-S-ELMs	98.07	492.21	98.05	0.11
	DBN	97.51	5013	97.53	0.04
	ErrCor	96.25	344.82	96.19	0.32
	PC-ELM	97.16	362.05	97.18	0.09
	OCI-ELM	97.59	327.2	97.62	0.05
	DOC-IELM-AEs	98.44	440.39	98.41	0.04
California Housing	SVM	96.56	327.17	96.56	0
	ELM	96.79	289.24	96.72	0.16
	ML-ELM	98.12	462.31	98.14	0.12
	AE-S-ELMs	98.26	402.49	98.23	0.09
	DBN	98.03	3989	98.04	0.08
	ErrCor	97.01	401.66	97.11	0.25
	PC-ELM	97.34	396.52	97.31	0.28
	OCI-ELM	97.92	386.63	97.90	0.11
	DOC-IELM-AEs	98.71	447.2	98.72	0.07
Concrete Compressive Strength	SVM	95.72	32.32	95.78	0.04
	ELM	95.91	28.29	96.02	0.06
	ML-ELM	97.26	53.4	97.33	0.18
	AE-S-ELMs	97.49	50.66	97.41	0.12
	DBN	96.64	320.04	96.58	0.05
	ErrCor	96.32	47.45	96.35	0.25
	PC-ELM	96.55	43.97	96.67	0.09
	OCI-ELM	97.04	44.33	97.15	0.06
	DOC-IELM-AEs	98.39	51.58	98.37	0.03
BlogFeedback	SVM	89.75	3906	89.82	0.03
	ELM	90.12	2405	90.14	0.22
	ML-ELM	91.86	5175	91.82	0.12
	AE-S-ELMs	91.83	5247	91.79	0.13
	DBN	90.51	19766	90.60	0.09
	ErrCor	90.39	4889	90.44	0.09
	PC-ELM	90.54	4308	90.58	0.12
	OCI-ELM	91.76	4223	91.82	0.09
	DOC-IELM-AEs	93.16	5271	93.27	0.07
Online News Popularity	SVM	91.75	311.83	91.72	0.04
	ELM	91.68	131.34	91.69	0.34
	ML-ELM	92.62	684.98	92.71	0.17
	AE-S-ELMs	92.59	685.62	95.72	0.12
	DBN	92.23	7062	92.26	0.05
	ErrCor	91.61	541.14	91.77	0.29
	PC-ELM	92.29	576.01	92.35	0.15
	OCI-ELM	92.52	521.24	92.54	0.12
	DOC-IELM-AEs	93.69	634.09	93.84	0.11