This research arises from the need to predict the amount of air pollutants in
meteorological stations. Air pollution depends on the location of the stations
(weather conditions and activities in the surroundings). Frequently, the
surrounding information is not considered in the learning process. This
information is known beforehand in the absence of unobserved weather conditions
and remains constant for the same station. Considering the surrounding
information as side information facilitates the generalization for predicting
pollutants in new stations, leading to a zero-shot regression scenario.
Available methods in zero-shot typically lean towards classification, and are
not easily extensible to regression. This paper proposes two zero-shot methods
for regression. The first method is a similarity based approach that learns
models from features and aggregates them using side information. However,
potential knowledge of the feature models may be lost in the aggregation. The
second method overcomes this drawback by replacing the aggregation procedure
and learning the correspondence between side information and feature-induced
models, instead. Both proposals are compared with a baseline procedure using
artificial datasets, UCI repository communities and crime datasets, and the
pollutants. Both approaches outperform the baseline method, but the parameter
learning approach manifests its superiority over the similarity based method.

通过使用侧信息的相似度和参数学习方法，本文提出了两种用于回归的零样本方法，用于预测气象站的空气污染物数量，并在人工数据集、UCI 存储库社区和犯罪数据集以及污染物方面进行了比较。这两种方法均优于基线方法，但参数学习方法表现出了较高的优越性。

零样本回归的目标归纳方法

Target inductive methods for zero-shot regression

Zero-shot learning provides models for targets for which instances are not
available, commonly called unobserved targets. The availability of target side
information becomes crucial in this context in order to properly induce models
for these targets. The literature is plenty of strategies to cope with this
scenario, but specifically designed on the basis of a zero-shot classification
scenario, mostly in computer vision and image classification, but they are
either not applicable or easily extensible for a zero-shot regression framework
for which a continuos value is required to be predicted rather than a label. In
fact, there is a considerable lack of methods for zero-shot regression in the
literature. Two approaches for zero-shot regression that work in a two-phase
procedure were recently proposed. They first learn the observed target models
through a classical regression learning ignoring the target side information.
Then, they aggregate those observed target models afterwards exploiting the
target side information and the models for the unobserved targets are induced.
Despite both have shown quite good performance because of the different
treatment they grant to the common features and to the side information, they
exploit features and side information separately, avoiding a global
optimization for providing the unobserved target models. The proposal of this
paper is a novel method that jointly takes features and side information in a
one-phase learning process, but treating side information properly and in a
more deserving way than as common features. A specific kernel that properly
merges features and side information is proposed for this purpose resulting in
a novel approach that exhibits better performance over both artificial and real
datasets.

零样本学习中缺乏零样本回归方法，本文提出了一种新颖的方法，将特征和附加信息融合成一个核，以更好地预测未观察到的目标模型。该方法在人工和真实数据集上展现出更好的性能。