Multi-task classification with infinite local experts
Multi-task classification with infinite local experts
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DOI：
10.1109/icassp.2009.4959897
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
Chunping Wang;Qi An;L. Carin;D. Dunson
中科院分区：
文献类型：
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作者：
Chunping Wang;Qi An;L. Carin;D. Dunson
我们提出了一个多任务学习（MTL）框架的非线性分类，基于一个无限的本地专家在特征空间。本地专家的使用使得能够在专家级别上共享，即使任务仅在特征空间的子区域中相似，也鼓励信息的借用。一个核棒断裂过程（KSBP）的前施加在底层分布的类标签，使专家的数量推断在后验，从而避免了模型选择问题。MTL是通过在任务相关KSBPs之上的层上施加狄利克雷过程（DP）来实现的。
We propose a multi-task learning (MTL) framework for non-linear classification, based on an infinite set of local experts in feature space. The usage of local experts enables sharing at the expert-level, encouraging the borrowing of information even if tasks are similar only in subregions of feature space. A kernel stick-breaking process (KSBP) prior is imposed on the underlying distribution of class labels, so that the number of experts is inferred in the posterior and thus model selection issues are avoided. The MTL is implemented by imposing a Dirichlet process (DP) prior on a layer above the task-dependent KSBPs.