Learning with Feature Feedback: from Theory to Practice

In supervised learning, a human annotator only needs to assign each data point (document, image, etc.) its correct label. But in many situations, the human can also provide richer feedback at essentially no extra cost. In this paper, we examine a particular type of feature feedback that has been used, with some success, in information retrieval and in computer vision. We formalize two models of feature feedback, give learning algorithms for them, and quantify their usefulness in the learning process. Our experiments also show the efficacy of these methods.

[1]  Jason Eisner,et al.  Modeling Annotators: A Generative Approach to Learning from Annotator Rationales , 2008, EMNLP.

[2]  Robert E. Schapire,et al.  Incorporating Prior Knowledge into Boosting , 2002, ICML.

[3]  Gideon S. Mann,et al.  Reducing Annotation Effort Using Generalized Expectation Criteria , 2007 .

[4]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Jeff Donahue,et al.  Annotator rationales for visual recognition , 2011, 2011 International Conference on Computer Vision.

[6]  Burr Settles,et al.  Active Learning , 2012, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[7]  Sanjoy Dasgupta,et al.  Two faces of active learning , 2011, Theor. Comput. Sci..

[8]  Gideon S. Mann,et al.  Learning from labeled features using generalized expectation criteria , 2008, SIGIR '08.

[9]  Foster J. Provost,et al.  Active feature-value acquisition for classifier induction , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[10]  Hema Raghavan,et al.  InterActive Feature Selection , 2005, IJCAI.

[11]  Rohini K. Srihari,et al.  Incorporating prior knowledge with weighted margin support vector machines , 2004, KDD.

[12]  Hema Raghavan,et al.  Active Learning with Feedback on Features and Instances , 2006, J. Mach. Learn. Res..

[13]  Burr Settles,et al.  Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances , 2011, EMNLP.

[14]  Foster J. Provost,et al.  An expected utility approach to active feature-value acquisition , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[15]  David A. Cohn,et al.  Improving generalization with active learning , 1994, Machine Learning.

[16]  Peter L. Bartlett,et al.  Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..

[17]  Vikas Sindhwani,et al.  Uncertainty sampling and transductive experimental design for active dual supervision , 2009, ICML '09.

[18]  Pietro Perona,et al.  Visual Recognition with Humans in the Loop , 2010, ECCV.

[19]  Yiming Yang,et al.  RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..

[20]  W. Bruce Croft,et al.  Experiments with query acquisition and use in document retrieval systems , 1989, SIGIR '90.

[21]  Carla E. Brodley,et al.  The Constrained Weight Space SVM: Learning with Ranked Features , 2011, ICML.