TY - GEN
T1 - SVM optimization
T2 - 25th International Conference on Machine Learning
AU - Shalev-Shwartz, Shai
AU - Srebro, Nathan
PY - 2008
Y1 - 2008
N2 - We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple subgradient descent approach indeed displays such behavior, at least for linear kernels.
AB - We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple subgradient descent approach indeed displays such behavior, at least for linear kernels.
UR - http://www.scopus.com/inward/record.url?scp=56449110590&partnerID=8YFLogxK
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:56449110590
SN - 9781605582054
T3 - Proceedings of the 25th International Conference on Machine Learning
SP - 928
EP - 935
BT - Proceedings of the 25th International Conference on Machine Learning
Y2 - 5 July 2008 through 9 July 2008
ER -