Designing adaptive classifiers for an evolving data stream is a challenging
task due to the data size and its dynamically changing nature. Combining
individual classifiers in an online setting, the ensemble approach, is a
well-known solution. It is possible that a subset of classifiers in the
ensemble outperforms others in a time-varying fashion. However, optimum weight
assignment for component classifiers is a problem which is not yet fully
addressed in online evolving environments. We propose a novel data stream
ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble
(GOOWE), which assigns optimum weights to the component classifiers using a
sliding window containing the most recent data instances. We map vote scores of
individual classifiers and true class labels into a spatial environment. Based
on the Euclidean distance between vote scores and ideal-points, and using the
linear least squares (LSQ) solution, we present a novel, dynamic, and online
weighting approach. While LSQ is used for batch mode ensemble classifiers, it
is the first time that we adapt and use it for online environments by providing
a spatial modeling of online ensembles. In order to show the robustness of the
proposed algorithm, we use real-world datasets and synthetic data generators
using the MOA libraries. First, we analyze the impact of our weighting system
on prediction accuracy through two scenarios. Second, we compare GOOWE with 8
state-of-the-art ensemble classifiers in a comprehensive experimental
environment. Our experiments show that GOOWE provides improved reactions to
different types of concept drift compared to our baselines. The statistical
tests indicate a significant improvement in accuracy, with conservative time
and memory requirements.

本文介绍了一种新的数据流集成分类器，Geometrically Optimum and Online-Weighted Ensemble (GOOWE)。GOOWE 通过使用包含最近数据实例的滑动窗口，为组成分类器分配最优权重。作者在真实世界数据集和 MOA 库的合成数据生成器中使用 GOOWE，通过两个情景分析了该权重系统对预测准确性的影响，并将其与 8 种其他集成分类器进行了比较。实验结果表明，与基线相比，GOOWE 在不同类型的概念漂移下提供了更好的反应，并且具有保守的时间和内存要求。

GOOWE: 面向动态数据流的几何最佳化和在线加权集成分类器

GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for  Evolving Data Streams

The recent success of deep neural networks relies on massive amounts of
labeled data. For a target task where labeled data is unavailable, domain
adaptation can transfer a learner from a different source domain. In this
paper, we propose a new approach to domain adaptation in deep networks that can
jointly learn adaptive classifiers and transferable features from labeled data
in the source domain and unlabeled data in the target domain. We relax a
shared-classifier assumption made by previous methods and assume that the
source classifier and target classifier differ by a residual function. We
enable classifier adaptation by plugging several layers into deep network to
explicitly learn the residual function with reference to the target classifier.
We fuse features of multiple layers with tensor product and embed them into
reproducing kernel Hilbert spaces to match distributions for feature
adaptation. The adaptation can be achieved in most feed-forward models by
extending them with new residual layers and loss functions, which can be
trained efficiently via back-propagation. Empirical evidence shows that the new
approach outperforms state of the art methods on standard domain adaptation
benchmarks.

本文提出了一种可以同时学习源域标记数据和目标域未标记数据的适应分类器和可转移特征的深度网络领域适应新方法。改进了以往方法假设源分类器和目标分类器共享分类器的假设，并通过将多个层的特征与张量积融合并将它们嵌入可再生核希尔伯特空间来匹配特征适应的分布。实验结果表明新方法在标准领域适应基准上优于现有最先进的方法。

带残差转移网络的无监督域自适应

Unsupervised Domain Adaptation with Residual Transfer Networks

In this correspondence, we will point out a problem with testing adaptive
classifiers on autocorrelated data. In such a case random change alarms may
boost the accuracy figures. Hence, we cannot be sure if the adaptation is
working well.

研究发现在自相关数据上测试自适应分类器会遇到随机变动告警导致准确率数据不可靠，从而无法确认自适应效果。