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首页> 外文会议>KI 2011: Advances in artificial intelligence >Speedy Local Search for Semi-Supervised Regularized Least-Squares
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Speedy Local Search for Semi-Supervised Regularized Least-Squares

机译：快速本地搜索半监督正则最小二乘

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摘要

In real-world machine learning scenarios, labeled data is often rare while unlabeled data can be obtained easily. Semi-supervised approaches aim at improving the prediction performance by taking both the labeled as well as the unlabeled part of the data into account. In particular, semi-supervised support vector machines favor decision hy-perplanes which lie in a "low-density area" induced by the unlabeled patterns (while still considering the labeled part of the data). The associated optimization problem, however, is of combinatorial nature and, hence, difficult to solve. In this work, we present an efficient implementation of a simple local search strategy that is based on matrix updates of the intermediate candidate solutions. Our experiments on both artificial and real-world data sets indicate that the approach can successfully incorporate unlabeled data in an efficient manner.
机译：在现实世界的机器学习场景中，带标签的数据通常很少见，而带标签的数据则很容易获得。半监督方法旨在通过考虑数据的标记部分和未标记部分来提高预测性能。特别是，半监督支持向量机偏向于决策超平面，该决策超平面位于由未标记模式引起的“低密度区域”中（同时仍在考虑数据的标记部分）。然而，相关的优化问题具有组合性质，因此难以解决。在这项工作中，我们提出了一个简单的本地搜索策略的有效实现，该策略基于中间候选解决方案的矩阵更新。我们在人工和现实数据集上的实验表明，该方法可以有效地成功合并未标记的数据。

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  • 来源
  • 会议地点 Berlin(DE);Berlin(DE);Berlin(DE)
  • 作者

    Fabian Gieseke; Oliver Kramer; Antti Airola; Tapio Pahikkala;

  • 作者单位

    Department Informatik Carl von Ossietzky Universitat Oldenburg 26111 Oldenburg, Germany;

    Department Informatik Carl von Ossietzky Universitat Oldenburg 26111 Oldenburg, Germany;

    Turku Centre for Computer Science, Department of Information Technology,University of Turku, 20520 Turku, Finland;

    Turku Centre for Computer Science, Department of Information Technology,University of Turku, 20520 Turku, Finland;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
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