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首页> 外文会议>International Conference on Artificial Neural Networks >Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines
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Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines

机译：离散随机搜索及其应用于深度关系机器的特征选择

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We use a model for discrete stochastic search in which one or more objects ("targets") are to be found by a search over n locations ("boxes"), where n is infinitely large. Each box has some probability that it contains a target, resulting in a distribution H over boxes. We model the search for the targets as a stochastic procedure that draws boxes using some distribution S. We derive first a general expression on the expected number of misses E[Z] made by the search procedure in terms of H and S. We then obtain an expression for an optimal distribution 5* to minimise E[Z]. This results in a relation between: the entropy of H and the KL-divergence between H and S*. This result induces a 2-partitions over the boxes consisting of those boxes with H probability greater than ~1/_n and the rest. We use this result to devise a stochastic search procedure for the practical situation when H is unknown. We present results from simulations that agree with theoretical predictions; and demonstrate that the expected misses by the optimal seeker decreases as the entropy of H decreases, with a maximum obtained for uniform H. Finally, we demonstrate applications of this stochastic search procedure with a coarse assumption about H. The theoretical results and the procedure are applicable to stochastic search over any aspect of machine learning that involves a discrete search-space: for example, choice over features, structures or discretized parameter-selection. In this work, the procedure is used to select features for Deep Relational Machines (DRMs) which are Deep Neural Networks (DNNs) defined in terms of domain-specific knowledge and built with features selected from large, potentially infinite-attribute space. Empirical results obtained across over 70 real-world datasets show that using the stochastic search procedure results in significantly better performances than the state-of-the-art.
机译：我们使用模型用于离散随机搜索，其中通过N个位置（“框”）搜索一个或多个对象（“目标”），其中n无限大。每个框都有一些概率，它包含目标，从而导致框中的分发H.我们将搜索目标的搜索作为使用一些分布S的随机程序绘制框。我们在H和S中搜索过程中的预期未命中的e [z]的预期次数普通表达式。我们获得最佳分布5 *以最小化E [Z]的表达式。这导致之间的关系：H和S *之间的H和KL分歧的关系。该结果引起了由具有H概率大于〜1 / _n的盒子的框中的2分区。我们使用此结果为H所未知的实际情况设计了随机搜索程序。我们呈现出同意理论预测的模拟结果;并证明最佳搜索器的预期未命中随着H的熵减少而降低，最大用于均匀H.最后，我们证明了该随机搜索过程的应用，粗糙的H.理论结果和程序是适用于随机搜索的机器学习的任何方面，涉及离散搜索空间的机器学习：例如，通过特征，结构或离散参数选择的选择。在这项工作中，该过程用于选择深度关系机（DRM）的功能，这些机器（DRM）是根据域特定知识（DNN）定义的深度神经网络（DNN），并使用从大型可能的无限属性空间中选择的功能构建。在70多个现实数据集中获得的经验结果表明，使用随机搜索程序的表现明显优于最先进的表现。

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