PAC-MDL bounds

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2777, Issue , 2003, Pages 344-357

  Blum, Avrim ^a   Langford, John ^b  

^a Carnegie Mellon University   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER PROGRAMMING LANGUAGES; DATA COMPRESSION; DATA TRANSFER; ERROR ANALYSIS; INFORMATION THEORY; THEOREM PROVING; COMPUTERS;

LABEL TRANSMISSION; NATURAL COMMUNICATION GAME; SHELL BOUNDS; STANDARD SAMPLE COMPLEXITY BOUNDS; NATURAL COMMUNICATION; PAC-BAYES; SAMPLE COMPLEXITY BOUNDS; STANDARD SAMPLES; UNLABELED DATA; VC DIMENSION;

LEARNING SYSTEMS; ARTIFICIAL INTELLIGENCE;

EID: 9444288055     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45167-9_26     Document Type: Conference Paper

References (9)

1. A. Blumer, A. Ehrenfeucht, D. Haussler, M. Warmuth. "Occam's Razor." Information Processing Letters 24: 377-380, 1987.
2. Thomas Cover and Joy Thomas, "Elements of Information Theory" Wiley, New York 1991.
3. Sally Floyd and Manfred Warmuth, "Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension", Machine Learning, Vol.21 (3), pp. 269-304.
4. G. E. Hinton and D. van Camp, "Keeping neural networks simple by minimizing the description length of the weights", COLT 1993.
5. John Langford "Quantitatively Tight Sample Complexity Bounds", Carnegie Mellon Thesis, 2002.
6. Nick Littlestone and Manfred Warmuth, "Relating Data Compression and Learnability", Unpublished manuscript. June 10, 1986.
7. David McAllester, "PAC-Bayesian Model Averaging" COLT 1999.
8. V. N. Vapnik and A. Y. Chervonenkis. "On the uniform convergence of relative frequencies of events to their probabilities." Theory of Probab. and its Applications, 16(2):264-280, 1971.
9. Vladimir N. Vapnik, "Statistical Learning Theory", Wiley, December 1999.