Unsupervised supervised learning I: Estimating classification and regression errors without labels

Journal of Machine Learning Research

Volumn 11, Issue , 2010, Pages 1323-1351

  Donmez, Pinar ^a   Lebanon, Guy ^b   Balasubramanian, Krishnakumar ^b  

^a Carnegie Mellon University ^*  (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

Classification and regression; Latent variable models; Maximum likelihood

Indexed keywords

CLASSIFICATION AND REGRESSION; CONSISTENCY RESULT; ERROR RATE; LATENT VARIABLE MODELS; MACHINE-LEARNING; REAL WORLD DATA; REGRESSION ERRORS; REGRESSION MODEL; UNLABELED DATA;

ESTIMATION; LEARNING ALGORITHMS; REGRESSION ANALYSIS; SUPERVISED LEARNING;

MAXIMUM LIKELIHOOD;

EID: 77951970416     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Y. Bishop, S. Fienberg, and P. Holland. Discrete Multivariate Analysis: Theory and Practice. MIT press, 1975.
2. J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proc. of ACL '07, 2007.
3. L. Breiman. Bias, variance, and arcing classifiers. Technical Report 460, Statistics department, University of California, 1996.
4. T. M. Cover and J. A. Thomas. Elements of Information Theory. John Wiley & Sons, second edition, 2005.
5. D. Cox, J. Little, and D. O'Shea. Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra. Springer, 2006.
6. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley New York, 2001.
7. B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall, 1997.
8. T. S. Ferguson. A Course in Large Sample Theory. Chapman & Hall, 1996.
9. D. J. Hand. Recent advances in error rate estimation. Pattern Recognition Letters, 4 (5): 335-346, 1986.
10. T. Joachims. Making large-scale svm learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT Press, 1999.
11. K. Lang. Newsweeder: Learning to filter netnews. In International Conference on Machine Learning, 1995.
12. A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, 1984.
13. V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? improving data quality and data mining using multiple, noisy labelers. In Proc. of the 14th ACM SIGKDD Internation Conference on Knowledge Discovery and Data Mining, pages 614-622, 2008.
14. P. Smyth, U. Fayyad, M. Burl, P. Perona, and P. Baldi. Inferring ground truth from subjective labelling of venus images. In Advances in Neural Information Processing Systems 7, 1995.
15. R. Snow, B. O'Connor, D. Jurafsky, and A. Y. Ng. Cheap and fast-but is it good? evaluating non-expert annotations for natural language tasks. In Proc. of EMNLP, 2008.
16. B. Sturmfels. Solving Systems of Polynomial Equations. American Mathematical Society, 2002. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, second edition, 2000.