May 26, 2009 � In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training�...
The Bregman divergence-based regularization is able to transfer the knowledge gained from training samples to testing samples by minimizing a distance between�...
In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training�...
This paper presents a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples�...
Bregman divergence based regularisation for transfer subspace learning. We propose subspace learning examples by utilising the proposed framework as a tool�...
Missing: Regularization | Show results with:Regularization
In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training�...
Bregman divergence-based regularization for transfer subspace learning. IEEE Transactions on Knowledge and Data Engineering, 22(7):929–942. Sugiyama, M�...
Abstract: This study presents a transfer learning method for addressing the insufficient sample problem in hyperspectral image classification.
In this paper, we propose a new transfer subspace learning method called feature reduction based transfer structural subspace learning (FRTSSL) for small-�...
Abstract—Transfer Subspace Learning has gained recent popularity in the literature for its ability to perform cross-dataset and cross-domain object�...
