摘要翻译:
本文提出了半监督支持向量机(S3VM)问题的二次规划近似,即近似QP-S3VM,它可以用现成的优化包有效地求解。我们证明了该近似公式建立了半监督学习(SSL)的低密度分离和基于图的模型之间的关系,这对于开发一个统一的半监督学习方法框架具有重要意义。在此基础上,我们提出了将SSL问题表示为子模集函数的新思想,并采用高效的子模优化算法对其进行求解。利用这种新的思想,我们将近似的QP-S3VM表示为一个子模集函数的最大化,这使得使用高效的贪婪算法进行优化成为可能。我们证明了所提出的方法是准确的,并且在时间复杂度方面比文献中的现有技术有显著的改进。
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英文标题:
《Submodular Optimization for Efficient Semi-supervised Support Vector
Machines》
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作者:
Wael Emara and Mehmed Kantardzic
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最新提交年份:
2011
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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:
In this work we present a quadratic programming approximation of the Semi-Supervised Support Vector Machine (S3VM) problem, namely approximate QP-S3VM, that can be efficiently solved using off the shelf optimization packages. We prove that this approximate formulation establishes a relation between the low density separation and the graph-based models of semi-supervised learning (SSL) which is important to develop a unifying framework for semi-supervised learning methods. Furthermore, we propose the novel idea of representing SSL problems as submodular set functions and use efficient submodular optimization algorithms to solve them. Using this new idea we develop a representation of the approximate QP-S3VM as a maximization of a submodular set function which makes it possible to optimize using efficient greedy algorithms. We demonstrate that the proposed methods are accurate and provide significant improvement in time complexity over the state of the art in the literature.
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PDF链接:
https://arxiv.org/pdf/1107.5236