The comparison of three Iterative Re-Weighted Least Square (IRWLS) methods for training SVMs proposed by Morales and Vazquez [2016, 2017]. Here, we highlight the interesting findings of the IRWLS methods compared to the standard software such as LibSVM, SVMLight, PS-SVM, and each other, if possible. The description of each column is as follows,

"Algorithm" indicates the corresponding algorithm's name or approach.

"Compared to LibSVM" shows the performance of the corresponding algorithm compared to LibSVM.

"Compared to SVMLight" describes the performance of the corresponding algorithm compared to SVMLight.

"Compared to PS-SVM" explains the performance of the corresponding algorithm compared to PS-SVM.

"Compared to each other" compares the performance of the corresponding algorithm to other algorithms in the table.

Note the table can be sorted by clicking on the title of each column.
Algorithm	Compared to LibSVM	Compared to SVMLight	Compared to PS-SVM	Compared to each other	Reference
Algorithm	Compared to LibSVM	Compared to SVMLight	Compared to PS-SVM	Compared to each other	Reference
PSIRWLS (Parallel Semi-parametric IRWLS)	Less efficiency using 500 centroids and one core 98% less runtime using 50 centroids and 16 nodes Up to 87% less runtime using 50 centroids and only one core Slightly lower accuracy Obtains fewer number of SVs The number of SVs and runtime scale better in terms of the number of training samples	Less efficiency using 500 centroids and only one core 99% less runtime using 50 centroids and 16 cores The number of SVs and runtime scale better in terms of the number of training samples	Parallel Cholesky factorization in PSIRWLS is faster than the parallel block matrix in PS-SVM 67% less runtime using 16 nodes and up to 47% less runtime using 8 cores Scales better in terms of the number of cores Linear speedup using 16 cores	PSIRWLS has the best speedup compared to PIRWLS and PS-SVM Has the best scalability All perform non-linear classifications	Morales and Vazquez [2016]
PIRWLS (Parallel IRWLS)	Less efficient for solving full SVMs using only one core Up to 78% less runtime using 16 cores Obtains fewer number of SVs The number of SVs and runtime scale better in terms of the number of training samples	Not efficient using only one core Up to 97% less runtime using 16 cores The runtime and the number of SVs scale better in terms of the number of training samples	PS-SVM outperforms PIRWLS using 1, 8, and 16 cores Obtains a larger number of SVs	PSIRWLS outperforms PIRWLS using 1, 8, and 16 cores PSIRWLS obtains fewer SVs Semi-parametric SVMs (PSIRWLS) is more efficient than solving full SVMs All perform non-linear classifications	Morales and Vazquez [2016]
LIBIRWLS (LIBrary IRWLS)	Outperforms LibSVM using only one core Up to 83% less runtime with comparative accuracy Obtains fewer SVs Scales better with the number of training samples	Outperforms SVMLight using only one core Up to 98% less runtime with comparative accuracy Obtains fewer SVs Scale better with the number of training samples	PS-SVM with 50 centroids outperforms LIBIRWLS with slightly lower accuracy	PSIRWLS with 50 centroids outperforms LIBIRWLS using 1 and 16 cores All perform non-linear classifications	Morales and Vazquez [2017]