A Kernel Approach to Multi-Task Learning with Task-Specific Kernels

Wei Wu; Hang Li; Yun-Hua Hu; Rong Jin

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A Kernel Approach to Multi-Task Learning with Task-Specific Kernels

机译：使用任务特定内核的多任务学习的内核方法

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Several kernel-based methods for multi-task learning have been proposed,which leverage relations among tasks as regularization to enhance the overall learning accuracies.These methods assume that the tasks share the same kernel,which could limit their applications because in practice different tasks may need different kernels.The main challenge of introducing multiple kernels into multiple tasks is that models from different reproducing kernel Hilbert spaces (RKHSs) are not comparable,making it difficult to exploit relations among tasks.This paper addresses the challenge by formalizing the problem in the square integrable space (SIS).Specially,it proposes a kernel-based method which makes use of a regularization term defined in SIS to represent task relations.We prove a new representer theorem for the proposed approach in SIS.We further derive a practical method for solving the learning problem and conduct consistency analysis of the method.We discuss the relationship between our method and an existing method.We also give an SVM (support vector machine)-based implementation of our method for multi-label classification.Experiments on an artificial example and two real-world datasets show that the proposed method performs better than the existing method.

机译：提出了几种基于内核的多任务学习方法，这些方法利用任务之间的关系作为正则化来增强总体学习准确性。这些方法假定任务共享同一内核，这可能会限制其应用，因为在实践中，不同的任务可能需要不同的内核。将多个内核引入多个任务的主要挑战是来自不同的再现内核希尔伯特空间（RKHS）的模型不具有可比性，从而难以利用任务之间的关系。本文通过将问题形式化来解决这一挑战。平方可积空间（SIS）。特别地，它提出了一种基于核的方法，该方法利用SIS中定义的正则化项来表示任务关系。我们证明了该方法在SIS中的新的表示定理。我们进一步推导了一种实用的方法为了解决学习问题并进行方法的一致性分析。我们讨论了方法之间的关系。 d和现有方法。我们还给出了基于SVM（支持向量机）的多标签分类方法的实现。在一个人工示例和两个真实数据集上的实验表明，该方法的性能优于现有方法。

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《计算机科学技术学报（英文版）》 |2012年第6期|1289-1301|共13页
作者
Wei Wu; Hang Li; Yun-Hua Hu; Rong Jin;
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作者单位

MOE-Microsoft Key Laboratory of Statistics and Information Technology, Department of Probability and Statistics School of Mathematical Sciences, Peking University, Beijing 100871, China;

Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China;

Noah's Ark Lab, Huawei, Hong Kong Science Park, Hong Kong, China;

Microsoft Research Asia, Beijing 100080, China;

Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, U.S.A.;

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入库时间 2024-01-27 02:56:49

A Kernel Approach to Multi-Task Learning with Task-Specific Kernels

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