COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ECML '07 Proceedings of the 18th European conference on Machine Learning
Active Learning from Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Analysis of Perceptron-Based Active Learning
The Journal of Machine Learning Research
Fast person-specific image retrieval using a simple and efficient clustering method
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A real-time face detection and recognition system for a mobile robot in a complex background
Artificial Life and Robotics
Unbiased online active learning in data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding rare classes: adapting generative and discriminative models in active learning
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Active learning with evolving streaming data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Learning to Group Web Text Incorporating Prior Information
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Large-scale live active learning: Training object detectors with crawled data and crowds
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CluChunk: clustering large scale user-generated content incorporating chunklet information
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
RALF: A reinforced active learning formulation for object class recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Mining millions of reviews: a technique to rank products based on importance of reviews
Proceedings of the 13th International Conference on Electronic Commerce
On active learning in hierarchical classification
Proceedings of the 21st ACM international conference on Information and knowledge management
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Active learning is a promising way to efficiently build up training sets with minimal supervision. Most existing methods consider the learning problem in a pool-based setting. However, in a lot of real-world learning tasks, such as crowdsourcing, the unlabeled samples, arrive sequentially in the form of continuous rapid streams. Thus, preparing a pool of unlabeled data for active learning is impractical. Moreover, performing exhaustive search in a data pool is expensive, and therefore unsuitable for supporting on-the-fly interactive learning in large scale data. In this paper, we present a systematic framework for stream-based multi-class active learning. Following the reinforcement learning framework, we propose a feedback-driven active learning approach by adaptively combining different criteria in a time-varying manner. Our method is able to balance exploration and exploitation during the learning process. Extensive evaluation on various benchmark and real-world datasets demonstrates the superiority of our framework over existing methods.