BibSonomy publications for /user/flawed/sample_selectionFri Oct 26 13:47:43 CEST 2007ECMLconf/ecml/2006695-702Lecture Notes in Computer ScienceActive Learning with Irrelevant Examples.42122006sample_selection active_learning Fri Jun 01 18:32:23 CEST 2007Journal of Machine Learning Research45-66Support Vector Machine Active Learning with Applications to Text Classification.22001feature_space svm active_learning sample_selection Wed May 30 16:40:56 CEST 2007Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07){Optimistic Active Learning using Mutual Information}2007mutual_information sample_selection imported active_learning Abstract An “active learning system” will sequentially decide which unlabeled instance to label, with the goal of efciently gathering the information necessary to produce a good classier. Some such systems greedily select the next instance based only on properties of that instance and the few currently labeled points — e.g., selecting the one closest to the current classication boundary. Unfortunately, these approaches ignore the valuable information contained in the other unlabeled instances, which can help identify a good classier much faster. For the previous approaches that do exploit this unlabeled data, this information is mostly used in a conservative way. One common property of the approaches in the literature is that the active learner sticks to one single query selection criterion in the whole process. We propose a system, MM+M, that selects the query instance that is able to provide the maximum conditional mutual information about the labels of the unlabeled instances, given the labeled data, in an optimistic way. This approach implicitly exploits the discriminative partition information contained in the unlabeled data. Instead of using one selection criterion, MM+M also employs a simple on-line method that changes its selection rule when it encounters an “unexpected label”. Our empirical results demonstrate that this new approach works effectively.Wed May 30 16:19:06 CEST 2007New York, NY, USAICML '04: Proceedings of the twenty-first international conference on Machine learning79Active learning using pre-clustering2004imported active_learning sample_selection clustering The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary, better performance can be achieved by taking into account the prior data distribution. The main contribution of the paper is a formal framework that incorporates clustering into active learning. The algorithm first constructs a classifier on the set of the cluster representatives, and then propagates the classification decision to the other samples via a local noise model. The proposed model allows to select the most representative samples as well as to avoid repeatedly labeling samples in the same cluster. During the active learning process, the clustering is adjusted using the coarse-to-fine strategy in order to balance between the advantage of large clusters and the accuracy of the data representation. The results of experiments in image databases show a better performance of our algorithm compared to the current methods.