标题： 快速交叉验证用于增量学习 显示英文标题

标题： Fast Cross-Validation for Incremental Learning

摘要： 交叉验证（CV）是机器学习中用于性能估计和参数调整的主要工具之一。 计算CV估计值的一般方法是对每个CV折叠分别运行学习算法，这是一个计算成本高昂的过程。 本文提出了一种新的方法来减轻基于CV的性能估计的计算负担。 与所有以前的努力不同，后者特定于某个特定的学习模型或问题领域，我们提出了一种通用的方法，适用于大量增量学习算法，这些算法非常适合大数据问题。 特别是，只要基础学习算法是增量的，我们的方法就可以应用于广泛的监督和无监督学习任务，具有不同的性能标准。 我们表明，该算法的运行时间按交叉验证折叠数的对数而不是线性增长。 此外，该算法具有有利的并行和分布式实现特性。 使用最先进的增量学习算法的实验验证了所提出方法的实用性。 显示英文摘要

摘要： Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning. The general recipe for computing CV estimate is to run a learning algorithm separately for each CV fold, a computationally expensive process. In this paper, we propose a new approach to reduce the computational burden of CV-based performance estimation. As opposed to all previous attempts, which are specific to a particular learning model or problem domain, we propose a general method applicable to a large class of incremental learning algorithms, which are uniquely fitted to big data problems. In particular, our method applies to a wide range of supervised and unsupervised learning tasks with different performance criteria, as long as the base learning algorithm is incremental. We show that the running time of the algorithm scales logarithmically, rather than linearly, in the number of CV folds. Furthermore, the algorithm has favorable properties for parallel and distributed implementation. Experiments with state-of-the-art incremental learning algorithms confirm the practicality of the proposed method.