The weighted k-nearest neighbors algorithm is one of the most fundamental
non-parametric methods in pattern recognition and machine learning. The
question of setting the optimal number of neighbors as well as the optimal
weights has received much attention throughout the years, nevertheless this
problem seems to have remained unsettled. In this paper we offer a simple
approach to locally weighted regression/classification, where we make the
bias-variance tradeoff explicit. Our formulation enables us to phrase a notion
of optimal weights, and to efficiently find these weights as well as the
optimal number of neighbors efficiently and adaptively, for each data point
whose value we wish to estimate. The applicability of our approach is
demonstrated on several datasets, showing superior performance over standard
locally weighted methods.

本文提出了一种简单的，能够使权重最优化的局部加权回归 / 分类方法，并能够为需要估计值的每个数据点高效地找到权重和最优化的邻居数量，从而在多个数据集上展示了比标准局部加权方法更优异的性能表现。

k*- 最近邻居：从全局到局部

k*-Nearest Neighbors: From Global to Local

In this paper, we focus on automatically detecting events in unconstrained
videos without the use of any visual training exemplars. In principle,
zero-shot learning makes it possible to train an event detection model based on
the assumption that events (e.g. \emph{birthday party}) can be described by
multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake").
Towards this goal, we first pre-train a bundle of concept classifiers using
data from other sources. Then we evaluate the semantic correlation of each
concept \wrt the event of interest and pick up the relevant concept
classifiers, which are applied on all test videos to get multiple prediction
score vectors. While most existing systems combine the predictions of the
concept classifiers with fixed weights, we propose to learn the optimal weights
of the concept classifiers for each testing video by exploring a set of online
available videos with free-form text descriptions of their content. To validate
the effectiveness of the proposed approach, we have conducted extensive
experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset.
The experimental results confirm the superiority of the proposed approach.

该研究旨在通过零样本学习自动检测视频中的事件，使用预先训练的概念分类器并学习对应权重以提高模型效果，在多个数据集上进行的实验表明该方法优于传统方法。