A Lazy Approach for Machine Learning Algorithms

机译：机器学习算法的一种惰性方法

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Most machine learning algorithms are eager methods in the sense that a model is generated with the complete training data set and, afterwards, this model is used to generalize the new test instances. In this work we study the performance of different machine learning algorithms when they are learned using a lazy approach. The idea is to build a classification model once the test instance is received and this model will only learn a selection of training patterns, the most relevant for the test instance. The method presented here incorporates a dynamic selection of training patterns using a weighting function. The lazy approach is applied to machine learning algorithms based on different paradigms and is validated in different classification domains.

机译：从使用完整的训练数据集生成模型的意义上讲，大多数机器学习算法都是急切的方法，然后，该模型用于泛化新的测试实例。在这项工作中，我们研究了使用懒惰方法学习不同机器学习算法时的性能。想法是一旦收到测试实例就建立一个分类模型，并且该模型将仅学习与测试实例最相关的训练模式的选择。此处介绍的方法使用权重函数结合了训练模式的动态选择。惰性方法应用于基于不同范式的机器学习算法，并在不同的分类域中得到了验证。

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《Artificial intelligence applications and innovations III》|2009年|517-522|共6页
会议地点 Thessaloniki(GR);Thessaloniki(GR)
作者
Ines M. Galvan; Jose M. Vails; Nicolas Lecomte; Pedro Isasi;
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Universidad Carlos III, Leganes (Madrid);

Universidad Carlos III, Leganes (Madrid);

Universidad Carlos III, Leganes (Madrid);

Universidad Carlos III, Leganes (Madrid);

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正文语种 eng
中图分类人工智能理论;
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A Lazy Approach for Machine Learning Algorithms

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