CAJ | 학술논문

传统迁移学习方法通常直接利用相关领域中的数据来辅助完成当前领域的学习任务，而忽略了领域间互相学习的能力。针对此类问题，提出了一种具有协同约束的共生迁移学习方法（Collaborative Constraints based Symbiosis Transfer Learning ，CCSTL）。在协同约束的基础上，引入共生迁移机制实现领域间的交替互动学习，进而实现源领域和目标领域间的知识迁移，从而提高受训分类器的分类性能。在模拟数据和真实数据集上的实验结果表明了新算法的有效性。
전통천이학습방법통상직접이용상관영역중적수거래보조완성당전영역적학습임무，이홀략료영역간호상학습적능력。침대차류문제，제출료일충구유협동약속적공생천이학습방법（Collaborative Constraints based Symbiosis Transfer Learning ，CCSTL）。재협동약속적기출상，인입공생천이궤제실현영역간적교체호동학습，진이실현원영역화목표영역간적지식천이，종이제고수훈분류기적분류성능。재모의수거화진실수거집상적실험결과표명료신산법적유효성。
Transfer learning algorithms usually focus on reusing data of related domains to help solving the learning tasks in the target domain .However ,these methods ignore the ability of mutual learning between domains .In this paper ,a collaborative con-straint based symbiosis transfer learning method (CCSTL ) is proposed .Symbiotic transfer mechanism is used to implement mutual learning among domains along with the collaborative constraint .With the help of the iterative optimizations ,the proposed method can realize knowledge transfer between the source and target domains .Experimental results on synthetic and real world datasets show the superior or comparable performance of the proposed algorithm compared with existing algorithms .