特征加权线性叠加

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摘要翻译：
集成方法，如堆叠，旨在通过混合多个机器学习模型的预测来提高预测精度。最近的工作表明，使用元特征，即描述数据集中每个示例的额外输入，可以提高集成方法的性能，但报告的最大收益来自需要大量调优和训练时间的非线性过程。在这里，我们提出了一种线性技术，特征加权线性堆叠(FWLS)，它结合了元特征来提高精度，同时保留了线性回归在速度、稳定性和可解释性方面众所周知的优点。FWLS使用本身是元特征线性函数的系数线性地组合模型预测。在最近结束的网飞奖比赛中，这项技术是第二名团队解决方案的一个关键方面。在Netflix Prize协同过滤数据集上，与标准线性堆叠相比，精确度有了显著提高。
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英文标题：
《Feature-Weighted Linear Stacking》
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作者：
Joseph Sill, Gabor Takacs, Lester Mackey, David Lin
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs describing each example in a dataset, can boost the performance of ensemble methods, but the greatest reported gains have come from nonlinear procedures requiring significant tuning and training time. Here, we present a linear technique, Feature-Weighted Linear Stacking (FWLS), that incorporates meta-features for improved accuracy while retaining the well-known virtues of linear regression regarding speed, stability, and interpretability. FWLS combines model predictions linearly using coefficients that are themselves linear functions of meta-features. This technique was a key facet of the solution of the second place team in the recently concluded Netflix Prize competition. Significant increases in accuracy over standard linear stacking are demonstrated on the Netflix Prize collaborative filtering dataset.
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PDF链接：
https://arxiv.org/pdf/0911.0460

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关键词：coefficients Intelligence Presentation Applications significant stacking dataset 加权 FWLS 训练