Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

Two-dimensional multilabel active learning with an efficient online adaptation model for image classification

10.1109/TPAMI.2008.218IEEE Transactions on Pattern Analysis and Machine Intelligence31101880-1897ITPI

Factores genéticos y ambientales modificadores de la toxicidad por amiloide en un modelo murino de enfermedad de Alzheimer

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Anatomía, Histología y Neurociencia. Fecha de lectura: 12 de Mayo de 201

Improving Active Learning With Sharp Data Reduction

Statistical analysis and pattern recognition have become a daunting endeavour in face of the enormous amount of information in datasets that have continually been made available. In view of the infeasibility of complete manual annotation, one seeks active learning methods for data organization, selection and prioritization that could help the user to label the samples. These methods, however, classify and reorganize the entire dataset at each iteration, and as the datasets grow, they become blatantly inefficient from the user's point of view. In this work, we propose an active learning paradigm which considerably reduces the non-annotated dataset into a small set of relevant samples for learning. During active learning, random samples are selected from this small learning set and the user annotates only the misclassified ones. A training set with new labelled samples increases at each iteration and improves the classifier for the next one. When the user is satisfied, the classifier can be used to annotate the rest of the dataset. To illustrate the effectiveness of this paradigm, we developed an instance based on the optimum path forest (OPF) classifier, while relying on clustering and classification for the learning process. By using this method, we were able to iteratively generate classifiers that improve quickly, to require few iterations, and to attain high accuracy while keeping user involvement to a minimum. We also show that the method provides better accuracies on unseen test sets with less user involvement than a baseline approach based on the OPF classifier and random selection of training samples from the entire dataset.PART 12734Angluin, D., Queries and concept learning (1988) Machine Learning, 2, pp. 319-342Cappabianco, F.A.M., Ide, J.S., Falcäo, A.X., Li, C.-S.R., Automatic subcortical tissue segmentation of mr images using optimum-path forest clustering (2011) International Conference on Image Processing (ICIP), pp. 2653-2656Cheng, Y., Mean shift, mode seeking, and clustering (1995) TPAMI, 17 (8), pp. 790-799Cohn, D.A., Ghahramani, Z., Jordan, M.I., Active learning with statistical models (1996) JAIR, 4, pp. 129-145Da Silva, A.T., Falcäo, A.X., Magalhäes, L.P., A new CBIR approach based on relevance feedback and optimum-path forest classification (2010) Journal of WSCG, pp. 73-80Da Silva, A.T., Falcäo, A.X., Magalhäes, L.P., Active learning paradigms for CBIR systems based on optimum-path forest classification (2011) Pattern Recognition, 44, pp. 2971-2978Davis, D.T., Hwang, J.N., Attentional focus training by boundary region data selection (1992) Intern. Joint Conference on Neural Networks (IJCNN), 1, pp. 676-681(2011) Biometrics Database Distribution, , www.nd.edu/~cvrl/CVRL/Data_Sets.html, Faces The Computer Vision Laboratory, University of Notre DameHolub, A., Perona, P., Burl, M.C., Entropy-based active learning for object recognition (2008) CVPRW, pp. 1-8Jain, P., Kapoor, A., Active learning for large multi-class problems (2009) IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 762-769Kapoor, A., Grauman, K., Urtasun, R., Darrell, T., Gaussian processes for object categorization (2010) International Journal of Computer Vision (IJCV), 88, pp. 169-188Li, X., Wang, L., Sung, E., Multi-label SVM active learning for image classification (2004) International Conference on Image Processing (ICIP), 4, pp. 2207-2210Papa, J.P., Falcäo, A.X., De Albuquerque, V.H.C., Tavares, J.M.R.S., Efficient supervised optimum-path forest classification for large datasets (2012) Pattern Recognition, 45, pp. 512-520Papa, J.P., Falcäo, A.X., Suzuki, C.T.N., Supervised pattern classification based on optimum-path forest (2009) Intern. Journal of Imaging Systems and Technology (IJIST), 19 (2), pp. 120-131(2011) Pen-Based Recognition of Handwritten Digits Dataset, , archive.ics.uci.edu/ml/datasets/Pen- Based+Recognition+of+Handwritten+Digits, PendigitsQi, G.-J., Hua, X.-S., Rui, Y., Tang, J., Zhang, H.-J., Two-dimensional multilabel active learning with an efficient online adaptation model for image classification (2009) IEEE Transact. on Pattern Analysis and Machine Intel., 31 (10), pp. 1880-1897Rocha, L.M., Cappabianco, F.A.M., Falcäo, A.X., Data clustering as an optimum-path forest problem with applications in image analysis (2009) Intern. Journal of Imaging Systems and Technology (IJIST), 19 (2), pp. 50-68Tong, S., Chang, E., Support vector machine active learning for image retrieval (2001) ICM, pp. 107-118. , ACMTong, S., Koller, D., Support vector machine active learning with applications to text classification (2002) Journal of Machine Learning Research (JMLR), 2, pp. 45-66Yan, R., Yang, J., Hauptmann, A., Automatically labeling video data using multi-class active learning (2003) IEEE Intern. Conference on Computer Vision (ICCV), 1, pp. 516-52