Active Learning with Statistical Models

Keywords

Computer science, Machine learning, Artificial intelligence, Artificial neural network, Regression

References

• Optimal control of systemsAnatoliy G. Butkovskiy — 1966
• Regression by local fitting: Methods, properties, and computational algorithmsWilliam S. Cleveland, Susan J. Devlin, Eric H. Grosse — 1988
• Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixturesSteven J. Nowlan — 1991
• Neural Network Exploration Using Optimal Experiment DesignDavid Cohn — 1993
• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paperA. P. Dempster, N. M. Laird, Donald B. Rubin — 1977
• Neural Networks and the Bias/Variance DilemmaStuart Geman, Elie Bienenstock, René Doursat — 1992
• Optimal Control SystemsDS Naidu, Ilya Kolmanovsky — 2018
• Neural net algorithms that learn in polynomial time from examples and queriesEric B. Baum — 1991
• Active Exploration in Dynamic EnvironmentsSebastian Thrun, Knut Möller — 1991
• Information-Based Objective Functions for Active Data SelectionDavid Mackay — 1992
• Bayesian Query Construction for Neural Network ModelsGerhard Paaß, Jörg Kindermann — 1994
• Supervised learning from incomplete data via an EM approachZoubin Ghahramani, Michael I. Jordan — 1993
• Training Connectionist Networks with Queries and Selective SamplingLes Atlas, David Cohn, Richard E. Ladner — 1989
• A general regression neural networkDonald F. Specht — 1991
• Minimizing Statistical Bias with QueriesDavid Cohn — 1996
• Robot juggling: implementation of memory-based learningStefan Schaal, Christopher G. Atkeson — 1994
• Statistical analysis of finite mixture distributionsBruce G. Lindsay, D. M. Titterington, A. F. M. Smith — 1986
• Selecting concise training sets from clean dataMark Plutowski, Hannah J. White — 1993
• Applied Linear RegressionSanford Weisberg — 2005
• Applied Linear Regression.Thomas Hassard, Sanford Weisberg — 1981

Cited by

• Support Vector Machine Concept-Dependent Active Learning for Image RetrievalEdward Yi Chang, Simon Tong, Chengwei Chang — 2005
• Un turc mécanique pour les ressources linguistiques : critique de la myriadisation du travail parcellisé (Mechanical Turk for linguistic resources: review of the crowdsourcing of parceled work)Benoît Sagot, Karën Fort, Gilles Adda — 2011
• Contributions à l'apprentissage par renforcement inverse. (Work on Inverse Reinforcement Learning)E. Klein — 2013
• Hybrid human-machine vision systems: image annotation using crowds, experts and machinesPietro Perona, Peter Welinder — 2012
• "Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language ProcessingMarvin Curtis — 2015
• Distributed Neural Processing Predictors of Multi-dimensional Properties of AffectMariano Rajoy Brey — 2017
• Maintenir la viabilité ou la résilience d'un système : les machines à vecteurs de support pour rompre la malédiction de la dimensionnalité ?Laëtitia Chapel — 2007
• Active data selection in supervised and unsupervised learningMartina Hasenjäger — 2000
• Extensions of Gaussian processes for ranking: semi-supervised and active learningWei Chu, Zoubin Ghahramani — 2005
• An Active Approach to Collaborative FilteringCraig Boutilier, Richard S. Zemel — 2003
• Live Monitoring: Using Adaptive Instrumentation and Analysis to Debug and Maintain Web ApplicationsEmre Kıcıman, Helen J. Wang — 2007
• Machine learning in health informatics: making better use of domain expertsCarla E. Brodley, Byron Wallace — 2012
• Annotation Time Stamps — Temporal Metadata from the Linguistic Annotation ProcessKatrin Tomanek, Udo Hahn — 2010
• Apprentissage Actif avec une Méthode de Réordonnancement pour l'Indexation et la Recherche de VidéosBahjat Safadi, Georges Quénot — 2011
• developmental active learning with intrinsic motivationPierre‐Yves Oudeyer, Adrien Baranès — 2008
• Une méthode paramétrique et robuste de classification semi-supervisée avec rejetChristophe Saint‐Jean, Carl Frélicot — 2001
• Mechanism Design and Analysis Using Simulation-Based Game ModelsMichael P. Wellman, Yevgeniy Vorobeychik — 2008
• Active Learning in Recurrent Neural Networks Facilitated by a Hebb-like Learning Rule with MemoryFrank Emmert‐Streib — 2005
• Learning Automated Product Recommendations Without Observable Features: An Initial Investigation.Mary S. Lee, Andrew W. Moore — 1995
• Active Learning For Outdoor Obstacle DetectionCristian Dima, Martial Hebert — 2005

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