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首页> 外文会议>International Computer Conference, Computer Society of Iran >FKNDT: A Flexible Kernel by Negotiating Between Data-dependent Kernel Learning and Task-dependent Kernel Learning
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FKNDT: A Flexible Kernel by Negotiating Between Data-dependent Kernel Learning and Task-dependent Kernel Learning

机译：FKNDT：通过在数据相关的内核学习和任务依赖性内核学习之间进行协商，灵活的内核

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摘要

Kernel learning is a challenging issue which has been vastly investigated over the last decades. The performance of kernel-based methods broadly relies on selecting an appropriate kernel. In machine learning community, a fundamental problem is how to model a suitable kernel. The traditional kernels, e.g., Gaussian kernel and polynomial kernel, are not adequately flexible to employ the information of the given data. Classical kernels are unable to sufficiently depict the characteristics of data similarities. To alleviate this problem, this paper presents a Flexible Kernel by Negotiating between Data-dependent kernel learning and Task-dependent kernel learning termed as FKNDT. Our method learns a suitable kernel by way of the Hadamard product of two types of kernels; a data-dependent kernel and a set of pre-specified classical kernels as a task-dependent kernel. We evaluate a flexible kernel in a supervised manner via Support Vector Machines (SVM). We model a learning process as a joint optimization problem including data-dependent kernel matrix learning, multiple kernel learning by means of quadratic programming, and standard SVM optimization. The experimental results demonstrate our technique provides a more effective kernel than the traditional kernels. Our method is better than other state-of-the-art kernel-based algorithms in terms of classification accuracy on fifteen benchmark datasets.
机译：内核学习是一个具有挑战性的问题，在过去的几十年中已经大大调查。基于内核的方法的性能广泛地依赖于选择适当的内核。在机器学习界中，基本问题是如何建模合适的内核。传统的内核，例如高斯内核和多项式内核，不能充分灵活地使用给定数据的信息。经典内核无法充分描述数据相似性的特征。为了缓解这个问题，本文通过在数据相关的内核学习和任务依赖性内核学习之间进行谈判来介绍一个灵活的内核。我们的方法通过两种类型的核的Hadamard产品来学习合适的内核;将数据相关的内核和一组预先指定的经典内核作为任务依赖性内核。我们通过支持向量机（SVM）以监督方式评估灵活的内核。我们一个学习的过程模型为包括相关数据核矩阵的学习，通过二次规划的手段多个内核学习和标准SVM优化的联合优化问题。实验结果表明我们的技术提供比传统内核更有效的核。在十五个基准数据集中的分类准确性方面，我们的方法优于其他基于最先进的内核算法。

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