An Active Learning Algorithm Based on Existing Training Data
An Active Learning Algorithm Based on Existing Training Data
复制标题
基于现有训练数据的主动学习算法
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
Tadao Nakamura
中科院分区：
文献类型：
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作者：
Hiroyuki Takizawa;T. Nakajima;Hiroaki Kobayashi;Tadao Nakamura
多层感知器通常被认为是一种被动学习器，只接收给定的训练数据。然而，如果一个多层感知器主动收集训练数据，解决其关于正在学习的问题的不确定性，那么就可以用更少的训练数据实现足够准确的分类。近年来，这种主动学习受到越来越多的关注。在本文中，我们提出了一种新的主动学习策略。该策略试图只产生对多层感知器有用的训练数据，以实现准确的分类，并避免产生冗余的训练数据。此外，该策略试图避免生成暂时有用的训练数据，这些数据在将来会变得多余。因此，该策略可以允许多层感知器以较少的训练数据实现准确的分类。为了证明与其他主动学习策略相比，该策略的性能，我们还提出了一种经验主动学习算法作为该策略的实现，它不需要昂贵的计算。实验结果表明，与传统的随机选择算法相比，该算法在训练数据较少的情况下提高了多层感知器的分类精度.实验结果表明，该算法的性能优于典型的主动学习算法。这些结果表明，该算法可以在较低的计算成本，因为训练数据的生成通常是昂贵的构造一个合适的训练数据集。相应地，该算法通过实验证明了该策略的有效性。我们还讨论了该算法的一些缺点。关键词：多层感知器，网络反演算法，主动学习，分类问题
A multilayer perceptron is usually considered a passive learner that only receives given training data. However, if a multilayer perceptron actively gathers training data that resolve its uncertainty about a problem being learnt, sufficiently accurate classification is attained with fewer training data. Recently, such active learning has been receiving an increasing interest. In this paper, we propose a novel active learning strategy. The strategy attempts to produce only useful training data for multilayer perceptrons to achieve accurate classification, and avoids generating redundant training data. Furthermore, the strategy attempts to avoid generating temporarily useful training data that will become redundant in the future. As a result, the strategy can allow multilayer perceptrons to achieve accurate classification with fewer training data. To demonstrate the performance of the strategy in comparison with other active learning strategies, we also propose an empirical active learning algorithm as an implementation of the strategy, which does not require expensive computations. Experimental results show that the proposed algorithm improves the classification accuracy of a multilayer perceptron with fewer training data than that for a conventional random selection algorithm that constructs a training data set without explicit strategies. Moreover, the algorithm outperforms typical active learning algorithms in the experiments. Those results show that the algorithm can construct an appropriate training data set at lower computational cost, because training data generation is usually costly. Accordingly, the algorithm proves the effectiveness of the strategy through the experiments. We also discuss some drawbacks of the algorithm. key words: multilayer perceptrons, the network inversion algorithm, active learning, classification problems