Export Reviews, Discussions, Author Feedback and Meta-Reviews

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper describes a framework particularly useful for semi-supervised learning based on Fredholm kernels. The classical supervised learning optimization problem solved in kernel-based methods is extended to incorporate unlabeled information leading to discretized version of the Fredholm integral equation. Quality The paper has high technical quality with well-supported claims by theoretical analysis and convincing experimental results. The proposed formulation leads to a new data-dependent kernel that incorporates unlabeled information.