Robust inference of relevant attributes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2842, Issue , 2003, Pages 99-113

  Arpe, Jan ^a   Reischuk, Rüdiger ^a  

^a UNIVERSITY OF LÜBECK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS; BIOLOGY; BOOLEAN FUNCTIONS; ERROR ANALYSIS; FUNCTIONS; VECTORS;

BINARY CLASSIFICATION; COMBINATORIAL PROPERTIES; COMPUTATIONAL BIOLOGY; GREEDY STRATEGIES; INPUT DISTRIBUTIONS; MEDICAL RESEARCH; ROBUST INFERENCE; UNIFORM DISTRIBUTION;

BOOLEAN FUNCTIONS; LEARNING SYSTEMS;

BINARY CLASSIFICATIONS; BOOLEAN INPUT; COMPUTATIONAL BIOLOGY;

EID: 7444229900     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39624-6_10     Document Type: Conference Paper

References (17)

1. R. Agrawal, T. Imielinski, and A. Swami, Mining Association Rules between Sets of Items in Large Databases. Proc. 1993 ACM SIGMOD Conf., 207–216
2. T. Akutsu, F. Bao, Approximating Minimum Keys and Optimal Substructure Screens. Proc. 2nd COCOON, Springer LNCS 1090 (1996), 290–299
3. T. Akutsu, S. Miyano, and S. Kuhara, A Simple Greedy Algorithm for Finding Functional Relations: Efficient Implementation and Average Case Analysis. TCS 292(2) (2003), 481–495. (see also Proc.3rd DS, Springer LNAI 1967 (2000), 86–98.)
4. D. Angluin, Queries and Concept Learning. Machine Learning 2(4) (1988), 319–342, Kluwer Academic Publishers, Boston
5. D. Angluin and P. Laird, Learning from noisy examples. Machine Learning 2(4) (1988), 343–370, Kluwer Academic Publishers, Boston
6. S. Arora, L. Babai, J. Stern, and Z. Sweedyk, The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations, J. CSS 54 (1997), 317–331
7. J. Arpe, R. Reischuk, Robust Inference of Relevant Attributes. Techn. Report, SIIM-TR-A 03-12, Univ. Lübeck, 2003, available at http://www.tcs.mu-luebeck.de/TechReports.html
8. A. Blum, L. Hellerstein, and N. Littlestone, Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. Proc. 4th COLT ’91, 157–166
9. A. Blum, P. Langley, Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1–2), 245–271 (1997)
10. U. Feige, A Threshold of ln n for Approximating Set Cover. J. ACM 45 (1998), 634–652
11. S. Goldman, H. Sloan, Can PAC Learning Algorithms Tolerate Random Attribute Noise? Algorithmica 14 (1995), 70–84
12. D. Johnson, Approximation Algorithms for Combinatorial Problems. J. CSS 9 (1974), 256–278
13. N. Littlestone, Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm. Machine Learning 4(2) (1988), 285–318, Kluwer Academic Publishers, Boston
14. N. Littlestone, From On-line to Batch Learning. Proc. 2nd COLT 1989, 269–284
15. H. Mannila, K. Räihä, On the Complexity of Inferring Functional Dependencies. Discrete Applied Mathematics 40 (1992), 237–243
16. E. Mossel, R. O’Donnell, R. Servedio, Learning Juntas. Proc. STOC ’03, 206–212
17. L. Valiant, Projection Learning. Machine Learning 37(2) (1999), 115–130, Kluwer Academic Publishers, Boston.