A Brief Introduction to Weakly Supervised Learning
نویسنده
چکیده
Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of data labeling process. Thus, it is desired for machine learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision where only a subset of training data are given with labels; inexact supervision where the training data are given with only coarse-grained labels; inaccurate supervision where the given labels are not always ground-truth.
منابع مشابه
Weakly Supervised Classification of Objects in Images Using Soft Random Forests
The development of robust classification model is among the important issues in computer vision. This paper deals with weakly supervised learning that generalizes the supervised and semi-supervised learning. In weakly supervised learning training data are given as the priors of each class for each sample. We first propose a weakly supervised strategy for learning soft decision trees. Besides, t...
متن کاملA General Formulation for Safely Exploiting Weakly Supervised Data
Weakly supervised data is an important machine learning data to help improve learning performance. However, recent results indicate that machine learning techniques with the usage of weakly supervised data may sometimes cause performance degradation. Safely leveraging weakly supervised data is important, whereas there is only very limited effort, especially on a general formulation to help prov...
متن کاملWeakly Supervised Learning for Hedge Classification in Scientific Literature
We investigate automatic classification of speculative language (‘hedging’), in biomedical text using weakly supervised machine learning. Our contributions include a precise description of the task with annotation guidelines, analysis and discussion, a probabilistic weakly supervised learning model, and experimental evaluation of the methods presented. We show that hedge classification is feasi...
متن کاملCollaborative Learning for Weakly Supervised Object Detection
Weakly supervised object detection has recently received much attention, since it only requires imagelevel labels instead of the bounding-box labels consumed in strongly supervised learning. Nevertheless, the save in labeling expense is usually at the cost of model accuracy. In this paper, we propose a simple but effective weakly supervised collaborative learning framework to resolve this probl...
متن کاملEECS 598 : Statistical Learning Theory , Winter 2014 Topic 20 Weakly Supervised Learning
Weakly supervised learning problems are between supervised and unsupervised learning problems. You can think of them as supervised learning problems where some label information is missing or has been contaminated in some way. We will focus on a specific weakly supervised learning problem, namely, binary classification with one-sided label noise, although the ideas here can be brought to bear o...
متن کامل