Budget Semi-supervised Learning
نویسندگان
چکیده
In this paper we propose to study budget semi-supervised learning, i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the computational resource that can be used is generally not unlimited. Effective budget semi-supervised learning algorithms should be able to adjust behaviors considering the given resource budget. Roughly, the more resource, the more exploitation on unlabeled data. As an example, in this paper we show that this is achievable by a simple yet effective method.
منابع مشابه
Storage Fit Learning with Unlabeled Data
By using abundant unlabeled data, semi-supervised learning approaches have been found useful in various tasks. Existing approaches, however, neglect the fact that the storage available for the learning process is different under different situations, and thus, the learning approaches should be flexible subject to the storage budget limit. In this paper, we focus on graph-based semi-supervised l...
متن کاملSemi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk
This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...
متن کاملSemi-Supervised Learning on a Budget: Scaling Up to Large Datasets
Internet data sources provide us with large image datasets which are mostly without any explicit labeling. This setting is ideal for semi-supervised learning which seeks to exploit labeled data as well as a large pool of unlabeled data points to improve learning and classification. While we have made considerable progress on the theory and algorithms, we have seen limited success to translate s...
متن کاملSemi-supervised Encrypted Traffic Classification Using Composite Features Set
Many network management tasks such as managing bandwidth budget and ensuring quality of service objectives rely on accurate classification of network traffic. But the statistical features of encrypted traffics are not stable and do not contain sufficient information for classification all the time. Some applications support multiple protocols, and the behaviors of these applications are complic...
متن کاملActive and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models
The goal of this paper is to simulate the benefits of jointly applying active learning (AL) and semi-supervised training (SST) in a new speech recognition application. Our data selection approach relies on confidence filtering, and its impact on both the acoustic and language models (AM and LM) is studied. While AL is known to be beneficial to AM training, we show that it also carries out subst...
متن کامل