A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
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For least squares support vector machine (LSSVM) the lack of sparse, while the standard sparse algorithm exist a problem that it need to mark all of training data. We propose an active learning algorithm based on LSSVM to solve sparse problem. This method first construct a minimum classification LSSVM, and then calculate the uncertainty of the sample, select the closest category to mark the sample surface, and finally joined the training set of labeled samples and the establishment of a new classifier, repeat the process until the model accuracy to meet Requirements. 6 provided in the UCI data sets on the experimental results show that the proposed method can effectively improve the sparsity of LSSVM, and can reduce the cost labeled samples.