Large-Scale Semi-Supervised Learning
نویسنده
چکیده
Labeling data is expensive, whilst unlabeled data is often abundant and cheap to collect. Semi-supervised learning algorithms that use both types of data can perform significantly better than supervised algorithms that use labeled data alone. However, for such gains to be observed, the amount of unlabeled data trained on should be relatively large. Therefore, making semi-supervised algorithms scalable is paramount. In this work we review several recent techniques for semisupervised learning, and methods for improving the scalability of these algorithms.
منابع مشابه
Noise-Robust Semi-Supervised Learning by Large-Scale Sparse Coding
This paper presents a large-scale sparse coding algorithm to deal with the challenging problem of noiserobust semi-supervised learning over very large data with only few noisy initial labels. By giving an L1-norm formulation of Laplacian regularization directly based upon the manifold structure of the data, we transform noise-robust semi-supervised learning into a generalized sparse coding prob...
متن کاملSERBoost: Semi-supervised Boosting with Expectation Regularization
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization principle, we propose a novel semi-supervised boosting method, called SERBoost that can be applied to large scale vision problems. The complexity is mainly dominated by the base learners. The algorithm provides a mar...
متن کاملSemi-Supervised Convex Training for Dependency Parsing
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discriminative, convex, semi-supervised learning algorithm can be obtained that is applicable to large-scale problems. To demonstrate the benefits of this approach, we apply the technique to learning dependency parsers from...
متن کاملLarge Scale Semi - supervised Linear SVM with Stochastic Gradient Descent ⋆
Semi-supervised learning tries to employ a large collection of unlabeled data and a few labeled examples for improving generalization performance, which has been proved meaningful in real-world applications. The bottleneck of exiting semi-supervised approaches lies in over long training time due to the large scale unlabeled data. In this article we introduce a novel method for semi-supervised l...
متن کاملSemi-supervised Relation Extraction with Large-scale Word Clustering
We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When training on different sizes of data, our semi-supervised approach consistently outperformed a st...
متن کامل