Premium
Active learning with support vector machines
Author(s) -
Kremer Jan,
Steenstrup Pedersen Kim,
Igel Christian
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1132
Subject(s) - machine learning , active learning (machine learning) , artificial intelligence , computer science , support vector machine , online machine learning , instance based learning , decision boundary , semi supervised learning , heuristics , relevance vector machine , point (geometry) , feature vector , kernel method , computational learning theory , labeled data , feature (linguistics) , mathematics , linguistics , philosophy , geometry , operating system
In machine learning, active learning refers to algorithms that autonomously select the data points from which they will learn. There are many data mining applications in which large amounts of unlabeled data are readily available, but labels (e.g., human annotations or results coming from complex experiments) are costly to obtain. In such scenarios, an active learning algorithm aims at identifying data points that, if labeled and used for training, would most improve the learned model. Labels are then obtained only for the most promising data points. This speeds up learning and reduces labeling costs. Support vector machine ( SVM ) classifiers are particularly well‐suited for active learning due to their convenient mathematical properties. They perform linear classification, typically in a kernel‐induced feature space, which makes expressing the distance of a data point from the decision boundary straightforward. Furthermore, heuristics can efficiently help estimate how strongly learning from a data point influences the current model. This information can be used to actively select training samples. After a brief introduction to the active learning problem, we discuss different query strategies for selecting informative data points and review how these strategies give rise to different variants of active learning with SVMs . This article is categorized under: Technologies > Machine Learning