Add to ORKGWe compare the practical performance of several recently proposed algorithms for active learning in the online setting. We consider two algorithms (and their combined variants) that are strongly online, in that they do not store any previously labeled examples, and for which formal guarantees have recently been proven under various assumptions. We perform an empirical evaluation on optical character recognition (OCR) data, an application that we argue to be appropriately served by online active learning. We compare the performance between the algorithm variants and show significant reductions in label-complexity over random sampling
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
Abstract We investigate online active learning techniques for classification tasks in data stream mi...
Recent decades have witnessed great success of machine learning, especially for tasks where large an...
We compare the practical performance of several recently proposed algorithms for active learning in ...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
PhD thesisMany practical problems such as forecasting, real-time decisionmaking, streaming data appl...
Online Active Learning (OAL) has been an important research area in machine learning, which aims to ...
This thesis studies three problems in online learning. For all the problems the proposed solutions a...
124 pagesThis dissertation focuses on sequential decision making for active learning and inference i...
We consider a learning setting of importance to large scale machine learning: potentially unlimited ...
In interactive machine learning, human users and learning algorithms work together in order to solve...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
This thesis studies active learning and confidence-rated prediction, and the interplay between these...
We are living in the Internet Age, in which information entities and objects are interconnected, the...
Abstract—Active learning methods have been considered with increased interest in the statistical lea...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
Abstract We investigate online active learning techniques for classification tasks in data stream mi...
Recent decades have witnessed great success of machine learning, especially for tasks where large an...
We compare the practical performance of several recently proposed algorithms for active learning in ...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
PhD thesisMany practical problems such as forecasting, real-time decisionmaking, streaming data appl...
Online Active Learning (OAL) has been an important research area in machine learning, which aims to ...
This thesis studies three problems in online learning. For all the problems the proposed solutions a...
124 pagesThis dissertation focuses on sequential decision making for active learning and inference i...
We consider a learning setting of importance to large scale machine learning: potentially unlimited ...
In interactive machine learning, human users and learning algorithms work together in order to solve...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
This thesis studies active learning and confidence-rated prediction, and the interplay between these...
We are living in the Internet Age, in which information entities and objects are interconnected, the...
Abstract—Active learning methods have been considered with increased interest in the statistical lea...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
Abstract We investigate online active learning techniques for classification tasks in data stream mi...
Recent decades have witnessed great success of machine learning, especially for tasks where large an...