Information-based objective functions for active data selection
Neural Computation
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Robustness of regularized linear classification methods in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query rewriting using active learning for sponsored search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Active learning with statistical models
Journal of Artificial Intelligence Research
Convex experimental design using manifold structure for image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
SED: supervised experimental design and its application to text classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Discriminative experimental design
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Active hashing and its application to image and text retrieval
Data Mining and Knowledge Discovery
Improving Text Classification Accuracy by Training Label Cleaning
ACM Transactions on Information Systems (TOIS)
Early active learning via robust representation and structured sparsity
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper we propose a non-greedy active learning method for text categorization using least-squares support vector machines (LSSVM). Our work is based on transductive experimental design (TED), an active learning formulation that effectively explores the information of unlabeled data. Despite its appealing properties, the optimization problem is however NP-hard and thus--like most of other active learning methods--a greedy sequential strategy to select one data example after another was suggested to find a suboptimum. In this paper we formulate the problem into a continuous optimization problem and prove its convexity, meaning that a set of data examples can be selected with a guarantee of global optimum. We also develop an iterative algorithm to efficiently solve the optimization problem, which turns out to be very easy-to-implement. Our text categorization experiments on two text corpora empirically demonstrated that the new active learning algorithm outperforms the sequential greedy algorithm, and is promising for active text categorization applications.