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首页> 外文期刊>Cybernetics, IEEE Transactions on >Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods
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Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods

机译：广义隐藏映射岭回归，基于知识的神经网络感应归纳学习，模糊系统和核方法

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摘要

Inductive transfer learning has attracted increasing attention for the training of effective model in the target domain by leveraging the information in the source domain. However, most transfer learning methods are developed for a specific model, such as the commonly used support vector machine, which makes the methods applicable only to the adopted models. In this regard, the generalized hidden-mapping ridge regression (GHRR) method is introduced in order to train various types of classical intelligence models, including neural networks, fuzzy logical systems and kernel methods. Furthermore, the knowledge-leverage based transfer learning mechanism is integrated with GHRR to realize the inductive transfer learning method called transfer GHRR (TGHRR). Since the information from the induced knowledge is much clearer and more concise than that from the data in the source domain, it is more convenient to control and balance the similarity and difference of data distributions between the source and target domains. The proposed GHRR and TGHRR algorithms have been evaluated experimentally by performing regression and classification on synthetic and real world datasets. The results demonstrate that the performance of TGHRR is competitive with or even superior to existing state-of-the-art inductive transfer learning algorithms.
机译：归纳迁移学习通过利用源域中的信息，在目标域中有效模型的训练上引起了越来越多的关注。但是，大多数转移学习方法都是针对特定模型开发的，例如常用的支持向量机，这使得该方法仅适用于采用的模型。在这方面，为了训练各种类型的经典智能模型，包括神经网络，模糊逻辑系统和核方法，引入了广义隐藏映射岭回归（GHRR）方法。此外，基于知识杠杆的转移学习机制与GHRR集成在一起，以实现归纳转移学习方法，称为转移GHRR（TGHRR）。由于归纳知识的信息比源域中的数据更清楚，更简洁，因此控制和平衡源域和目标域之间数据分布的相似性和差异性更加方便。拟议的GHRR和TGHRR算法已通过对合成和真实数据集进行回归和分类进行了实验评估。结果表明，TGHRR的性能与现有的最新归纳转移学习算法相比甚至更高。

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