Unsupervised active learning techniques for labeling training sets: An experimental evaluation on sequential data
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Vinícius M. A. de Souza | Gustavo E. A. P. A. Batista | Rafael Geraldeli Rossi | Solange Oliveira Rezende | Vinicius M. A. Souza | R. G. Rossi | S. O. Rezende
[1] Jun Wang,et al. On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case , 2015, SDM.
[2] María José del Jesús,et al. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets , 2008, Fuzzy Sets Syst..
[3] Kwang Ryel Ryu,et al. Using Cluster-Based Sampling to Select Initial Training Set for Active Learning in Text Classification , 2004, PAKDD.
[4] Eamonn J. Keogh,et al. LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures , 2006, VLDB.
[5] L. E. Chadwick,et al. The effects of atmospheric pressure and composition on the flight of Drosophila. , 1949, The Biological bulletin.
[6] Dana Angluin. Queries revisited , 2004, Theor. Comput. Sci..
[7] Lei Chen,et al. Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.
[8] Henrik Boström,et al. Learning First Order Logic Time Series Classifiers: Rules and Boosting , 2000, PKDD.
[9] Tianshun Yao,et al. Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification , 2008, COLING.
[10] Eamonn J. Keogh,et al. CID: an efficient complexity-invariant distance for time series , 2013, Data Mining and Knowledge Discovery.
[11] Arnold W. M. Smeulders,et al. Active learning using pre-clustering , 2004, ICML.
[12] Edward Y. Chang,et al. Distance-function design and fusion for sequence data , 2004, CIKM '04.
[13] Anil K. Jain. Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..
[14] Bin Li,et al. A survey on instance selection for active learning , 2012, Knowledge and Information Systems.
[15] Daniel P. W. Ellis,et al. Exploring Low Cost Laser Sensors to Identify Flying Insect Species , 2015, J. Intell. Robotic Syst..
[16] Christos Faloutsos,et al. Fast subsequence matching in time-series databases , 1994, SIGMOD '94.
[17] Witold Pedrycz,et al. Particle Competition and Cooperation in Networks for Semi-Supervised Learning , 2012, IEEE Trans. Knowl. Data Eng..
[18] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[19] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[20] Celso André R. de Sousa,et al. Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study , 2014, 2014 22nd International Conference on Pattern Recognition.
[21] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[22] Nicolas Le Roux,et al. Efficient Non-Parametric Function Induction in Semi-Supervised Learning , 2004, AISTATS.
[23] Rong Jin,et al. Batch mode active learning and its application to medical image classification , 2006, ICML.
[24] Edwin Lughofer,et al. Hybrid active learning for reducing the annotation effort of operators in classification systems , 2012, Pattern Recognit..
[25] Daniel P. W. Ellis,et al. Support vector machine active learning for music retrieval , 2006, Multimedia Systems.
[26] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[27] Diego R. Amancio,et al. Probing the Topological Properties of Complex Networks Modeling Short Written Texts , 2014, PloS one.
[28] Daphne Koller,et al. Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..
[29] Xihong Wu,et al. On the importance of components of the MFCC in speech and speaker recognition , 2000, INTERSPEECH.
[30] Donghai Guan,et al. Initial training data selection for active learning , 2011, ICUIMC '11.
[31] William A. Gale,et al. A sequential algorithm for training text classifiers , 1994, SIGIR '94.
[32] Yannis Manolopoulos,et al. Feature-based classification of time-series data , 2001 .
[33] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[34] Dimitrios Gunopulos,et al. Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.
[35] Liang Zhao,et al. Detecting and labeling representative nodes for network-based semi-supervised learning , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[36] J. H. Ward. Hierarchical Grouping to Optimize an Objective Function , 1963 .
[37] Andrew McCallum,et al. Toward Optimal Active Learning through Sampling Estimation of Error Reduction , 2001, ICML.
[38] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[39] Mark Newman,et al. Networks: An Introduction , 2010 .
[40] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[41] Douglas J. Klein,et al. Centrality measure in graphs , 2010 .
[42] Pedro Jussieu de Rezende,et al. Robust active learning for the diagnosis of parasites , 2015, Pattern Recognit..
[43] Mark Craven,et al. Multiple-Instance Active Learning , 2007, NIPS.
[44] Eamonn J. Keogh,et al. Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.
[45] Ran El-Yaniv,et al. Online Choice of Active Learning Algorithms , 2003, J. Mach. Learn. Res..
[46] KENNETH MELLANBY,et al. Humidity and Insect Metabolism , 1936, Nature.
[47] Lei Chen,et al. On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.
[48] H. Sebastian Seung,et al. Query by committee , 1992, COLT '92.
[49] Wei Hu,et al. Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[50] Hui Ding,et al. Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..
[51] Xiaodong Lin,et al. Active Learning from Data Streams , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[52] L. Taylor,et al. ANALYSIS OF THE EFFECT OF TEMPERATURE ON INSECTS IN FLIGHT , 1963 .
[53] Celso André R. de Sousa,et al. An experimental analysis on time series transductive classification on graphs , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[54] Alneu de Andrade Lopes,et al. Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts , 2016, Inf. Process. Manag..
[55] Sofus A. Macskassy. Using graph-based metrics with empirical risk minimization to speed up active learning on networked data , 2009, KDD.
[56] Yannis Theodoridis,et al. Index-based Most Similar Trajectory Search , 2007, 2007 IEEE 23rd International Conference on Data Engineering.
[57] Vinícius M. A. de Souza,et al. Classification of Data Streams Applied to Insect Recognition: Initial Results , 2013, 2013 Brazilian Conference on Intelligent Systems.
[58] Sergey Brin,et al. The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.
[59] Rong Yan,et al. Automatically labeling video data using multi-class active learning , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[60] Alessandro Laio,et al. Clustering by fast search and find of density peaks , 2014, Science.
[61] Vinícius M. A. de Souza,et al. Time Series Classification Using Compression Distance of Recurrence Plots , 2013, 2013 IEEE 13th International Conference on Data Mining.
[62] Li Wei,et al. Fast time series classification using numerosity reduction , 2006, ICML.
[63] J. Berger,et al. The thirteen colors of timbre , 2005, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2005..
[64] Douglas A. Reynolds,et al. Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.
[65] Juan José Rodríguez Diez,et al. Interval and dynamic time warping-based decision trees , 2004, SAC '04.
[66] Larry D. Hostetler,et al. The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.
[67] Mark Craven,et al. Active Learning with Real Annotation Costs , 2008 .
[68] Brian Mac Namee,et al. Off to a Good Start: Using Clustering to Select the Initial Training Set in Active Learning , 2010, FLAIRS.
[69] Padhraic Smyth,et al. Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models , 2004, UAI 2004.
[70] Daniel P. W. Ellis,et al. Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps , 2013, 2013 12th International Conference on Machine Learning and Applications.
[71] Mark Craven,et al. An Analysis of Active Learning Strategies for Sequence Labeling Tasks , 2008, EMNLP.