In this paper we investigate the regularization properties of semiiterative regularization methods in Hilbert scales for linear ill-posed problems and perturbed data. It is well known that Landweber iteration can be remarkably accelerated by polynomial acceleration methods leading to the notion of optimal speed of convergence, which can be obtained by several efficient two-step methods, e.g., t...

This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further assist the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement a...

The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization principle, we propose a novel semi-supervised boosting method, called SERBoost that can be applied to large scale vision problems. The complexity is mainly dominated by the base learners. The algorithm provides a mar...

Manifold regularization (Belkin et al., 2006) is a geometrically motivated framework for machine learning within which several semi-supervised algorithms have been constructed. Here we try to provide some theoretical understanding of this approach. Our main result is to expose the natural structure of a class of problems on which manifold regularization methods are helpful. We show that for suc...

Location plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semisupervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the...

We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multiview scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning. The function optimization problem in a reproducing kern...

We propose a novel information theoretic approach for semi-supervised learning of conditional random fields that defines a training objective to combine the conditional likelihood on labeled data and the mutual information on unlabeled data. In contrast to previous minimum conditional entropy semi-supervised discriminative learning methods, our approach is grounded on a more solid foundation, t...

Most text categorization methods require text content of documents that is often difficult to obtain. We consider “Collaborative Text Categorization”, where each document is represented by the feedback from a large number of users. Our study focuses on the semisupervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this pr...

We first analyze the limits of learning in high dimension. Hence, we stress the difference between high dimensional ambient space and intrinsic geometry associated to the marginal distribution. We observe that, in the semi-supervised setting, unlabeled data could be used to exploit low dimensionality of the intrinsic geometry. In order to formalize these intuitions we briefly introduce the mani...