A framework for average case analysis of conjunctive learning algorithms
A framework for average case analysis of conjunctive learning algorithms
复制标题
联合学习算法平均案例分析框架
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Wendy Sarrett
中科院分区：
文献类型：
--
作者：
M. Pazzani;Wendy Sarrett
我们提出了一种对学习算法的平均案例行为建模的方法。我们的动机是预测学习算法的预期精度作为训练样本数量的函数。我们将这个框架应用于纯经验学习算法(纯合取概念的单边算法)，以及结合了经验学习和基于解释的学习的算法。该模型用于深入了解这些算法在一系列问题上的行为。最后，我们评估了当训练样本违反模型的假设时，平均案例模型的执行情况。
We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the number of training examples. We apply this framework to a purely empirical learning algorithm, (the one-sided algorithm for pure conjunctive concepts), and to an algorithm that combines empirical and explanation-based learning. The model is used to gain insight into the behavior of these algorithms on a series of problems. Finally, we evaluate how well the average case model performs when the training examples violate the assumptions of the model.