A Binary Classification Framework for Two-Stage Multiple Kernel Learning
By Abhishek Kumar, Alexandru Niculescu-Mizil, Koray Kavukcuoglu, Hal Daumé Iii. 2012
60 citations recorded by the source corpus.
With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of prespecified base kernels that is suitable for the task at hand has received significant attention from researchers. In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Framing MKL in this way has the distinct advantage that it makes it easy to leverage the extensive research in binary classification to develop better performing and more scalable MKL algorithms that are conceptually simpler, and, arguably, more accessible to practitioners. Experiments on nine data sets from different domains show that, despite its simplicity, the proposed technique compares favorably with current leading MKL approaches.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
- Jaime G. Carbonell
- Keerthiram Murugesan
- Kiri L. Wagstaff
- Luke S. Zettlemoyer
- Noah A. Smith
- Jianfeng Gao
- Mohit Bansal
- Graham Neubig
In Computer Vision and Pattern Recognition
Citations in the corpus
- Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions (2016, neutral): “, 2008), supervised graph inference (Biau & Bleakley, 2006) and multiple kernel learning (Kumar et al., 2012), to name a few.”
- Multi-Task Multiple Kernel Relationship Learning (2016, methodology): “For large-scale applications such as object detection, we introduce a novel two-stage online learning al-gorithm based on recent work [14] that learns the kernel weights independently from the model parameters.”