TY - GEN
T1 - Localized multiple kernel learning
AU - Gönen, Mehmet
AU - Alpaydin, Ethem
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kernel over the whole input space. In this paper, we develop a localized multiple kernel learning (LMKL) algorithm using a gating model for selecting the appropriate kernel function locally. The localizing gating model and the kernel-based classifier are coupled and their optimization is done in a joint mariner. Empirical results on ten benchmark and two bioinformatics data sets validate the applicability of our approach. LMKL achieves statistically similar accuracy results compared with MKL by storing fewer support vectors. LMKL can also combine multiple copies of the same kernel function localized in different parts. For example, LMKL with multiple linear kernels gives better accuracy results than using a single linear kernel on bioinformatics data sets.
AB - Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kernel over the whole input space. In this paper, we develop a localized multiple kernel learning (LMKL) algorithm using a gating model for selecting the appropriate kernel function locally. The localizing gating model and the kernel-based classifier are coupled and their optimization is done in a joint mariner. Empirical results on ten benchmark and two bioinformatics data sets validate the applicability of our approach. LMKL achieves statistically similar accuracy results compared with MKL by storing fewer support vectors. LMKL can also combine multiple copies of the same kernel function localized in different parts. For example, LMKL with multiple linear kernels gives better accuracy results than using a single linear kernel on bioinformatics data sets.
UR - http://www.scopus.com/inward/record.url?scp=56449124689&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56449124689&partnerID=8YFLogxK
U2 - 10.1145/1390156.1390201
DO - 10.1145/1390156.1390201
M3 - Conference contribution
AN - SCOPUS:56449124689
SN - 9781605582054
T3 - Proceedings of the 25th International Conference on Machine Learning
SP - 352
EP - 359
BT - Proceedings of the 25th International Conference on Machine Learning
PB - Association for Computing Machinery (ACM)
T2 - 25th International Conference on Machine Learning
Y2 - 5 July 2008 through 9 July 2008
ER -