Definition: 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.
Input: Abstract The nearest neighbor rule is one of the most widely used models for classification, and selecting a compact set of prototype instances is a primary challenges for its applications. Many existing approaches for prototype selection exploit instance-based analyses and locally-defined criteria on the class distribution, which are intractable for numerical optimization techniques. In this paper, we explore a parametric framework with an adjusted nearest neighbor rule, in which the selection of the neighboring prototypes is modified by their respective parameters. The framework allows us to formulate a minimization problem of the violation of the adjusted nearest neighbor rule over the training set with regards to numerical parameters. We show that the problem reduces to a large-margin principled learning and demonstrate its advantage by empirical comparisons with recent state-ofthe-art methods using public benchmark data.

Discriminative Learning of the Prototype Set for Nearest Neighbor Classification