2012/06/27 by Abhishek Kumar, Alexandru Niculescu-Mizil, Kumar, Abhishek +6 · 1 citation
Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1206.6428
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)
arxiv created 2012/06/27 · openalex publication_date 2012/06/27 · arxiv updated 2012/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 pre-specified 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.