Bayesian Optimal Active Search and Surveying
Bayesian Optimal Active Search and Surveying
复制标题
贝叶斯最优主动搜索和调查
DOI：
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发表时间：
2012
期刊：
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通讯作者：
R. Mann
中科院分区：
文献类型：
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作者：
R. Garnett;Yamuna Krishnamurthy;Xuehan Xiong;J. Schneider;R. Mann
我们考虑两个积极的二进制分类问题与非典型目标。在第一个主动搜索中，我们的目标是主动发现尽可能多的给定类的成员。在第二个主动调查中，我们的目标是主动查询点，以最终预测给定类的比例。许多现实世界的问题都可以用这些术语来描述，在任何一种情况下，典型的基于模型的问题（如泛化误差）都只是次要的。
我们通过贝叶斯决策理论来解决这些问题，在选择自然效用函数后，我们得到最优策略。我们提供三个贡献。除了引入主动测量问题，我们扩展了以前的工作主动搜索两种方式。首先，我们证明了一个新的理论结果，少近视近似的最优策略可以优于任何程度的近视近似。然后，我们推导出的界限，对于某些模型，使我们能够减少（在实践中显着）的指数搜索空间所需的天真的实现的最优策略，使进一步的前瞻性，同时仍然确保最佳决策总是。
We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as possible. In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class. Numerous real-world problems can be framed in these terms, and in either case typical model-based concerns such as generalization error are only of secondary importance.
We approach these problems via Bayesian decision theory; after choosing natural utility functions, we derive the optimal policies. We provide three contributions. In addition to introducing the active surveying problem, we extend previous work on active search in two ways. First, we prove a novel theoretical result, that less-myopic approximations to the optimal policy can outperform more-myopic approximations by any arbitrary degree. We then derive bounds that for certain models allow us to reduce (in practice dramatically) the exponential search space required by a naive implementation of the optimal policy, enabling further lookahead while still ensuring that optimal decisions are always made.