2015/06/30 by Pooria Joulani, András György, Joulani, Pooria +3
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Text and Document Classification Technologies #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1507.00066
Appearing in the International Joint Conference on Artificial Intelligence (IJCAI-2015), Buenos Aires, Argentina, July 2015
arxiv created 2015/06/30 · openalex publication_date 2015/06/30 · arxiv updated 2015/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.