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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Multiple kernel learning ： ウィキペディア英語版 Multiple kernel learning Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources (e.g. sound and images from a video) that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source. Multiple kernel learning approaches have been used in many applications, such as event recognition in video..,〔Lin Chen, Lixin Duan, and Dong Xu, "Event Recognition in Videos by Learning From Heterogeneous Web Sources," in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2666-2673〕 object recognition in images,〔Serhat S. Bucak, Rong Jin, and Anil K. Jain, Multiple Kernel Learning for Visual Object Recognition: A Review. T-PAMI, 2013.〕 and biomedical data fusion.〔Yu et al. (L2-norm multiple kernel learning and its application to biomedical data fusion ). BMC Bioinformatics 2010, 11:309〕 ==Algorithms== Multiple kernel learning algorithms have been developed for supervised, semi-supervised, as well as unsupervised learning. Most work has been done on the supervised learning case with linear combinations of kernels, however, many algorithms have been developed. The basic idea behind multiple kernel learning algorithms is to add an extra parameter to the minimization problem of the learning algorithm. As an example, consider the case of supervised learning of a linear combination of a set of $n$ kernels $K$. We introduce a new kernel $K\text{'}=\backslash sum\_^n\backslash beta\_iK\_i$, where $\backslash beta\_i$ is a vector of coefficients for each kernel. Because the kernels are additive (due to properties of reproducing kernel Hilbert spaces), this new function is still a kernel. For a set of data $X$ with labels $Y$, the minimization problem can then be written as :$\backslash min\_\backslash Epsilon(Y,\; K\text{'}c)+R(K,c)$ where $\backslash Epsilon$ is an error function and $R$ is a regularization term. $\backslash Epsilon$ is typically the square loss function (Tikhonov regularization) or the hinge loss function (for SVM algorithms), and $R$ is usually an $\backslash ell\_n$ norm or some combination of the norms (i.e. elastic net regularization). This optimization problem can then be solved by standard optimization methods. Adaptations of existing techniques such as the Sequential Minimal Optimization have also been developed for multiple kernel SVM-based methods.〔Francis R. Bach, Gert R. G. Lanckriet, and Michael I. Jordan. 2004. (Multiple kernel learning, conic duality, and the SMO algorithm ). In Proceedings of the twenty-first international conference on Machine learning (ICML '04). ACM, New York, NY, USA〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Multiple kernel learning」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.