Q: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Semi-supervised learning based on the low-density separation principle such as<lb>the cluster and manifold assumptions has been extensively studied in the last<lb>decades. However, such semi-supervised learning methods do not always per-<lb>form well due to violation of the cluster and manifold assumptions. In this paper,<lb>we propose a novel approach to semi-supervised learning that does not require<lb>such restrictive assumptions. Our key idea is to combine learning from positive<lb>and negative data (standard supervised learning) and learning from positive and<lb>unlabeled data (PU learning), the latter is guaranteed to be able to utilize unla-<lb>beled data without the cluster and manifold assumptions. We theoretically and<lb>experimentally show the usefulness of our approach.
A:
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning