Active Learning with Local Models

Neural Processing Letters

Volumn 7, Issue 2, 1998, Pages 107-117

  Hasenjäger, Martina ^a   Ritter, Helge ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Active learning; Local models; Query based learning; Vector quantization

Indexed keywords

COMPUTER SIMULATION; DRAWING (GRAPHICS); GEOMETRY; LEARNING ALGORITHMS; PATTERN RECOGNITION; VECTOR QUANTIZATION;

ACTIVE LEARNING; LOCAL MODELS; QUERY BASED LEARNING; VORONOI TESSELLATION;

LEARNING SYSTEMS;

EID: 0032047479     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1009688513124     Document Type: Article

References (14)

1. W. Kinzel and P. Ruján, "Improving a network generalization ability by selecting examples", Europhysics Letters, Vol. 13, pp. 473-477, 1990.
2. E.B. Baum, "Neural net algorithms that learn in polynomial time from examples and queries", IEEE Transactions on Neural Networks, Vol. 2, pp. 5-19, 1991.
3. J.-N. Hwang, J.J. Choi, S. Oh, and R.J. Marks II, "Query-based learning applied to partially trained multilayer perceptrons", IEEE Transactions on Neural Networks, Vol. 2, pp. 131-136, 1991.
4. H.S. Seung, M. Opper, and H. Sompolinsky, "Query by committee", in Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, pp. 287-294, ACM Pr., New York, NY, 1992.
5. Y. Freund, H.S. Seung, E. Shamir, and N. Tishby, "Information, prediction, and query by committee", in S.J. Hanson, J.D. Cowan, and C.L. Giles (eds) Neural Information Processing Systems, Vol. 5, pp. 483-490, Morgan Kaufmann, San Mateo, CA, 1993.
6. D.A. Cohn, "Neural network exploration using optimal experiment design", Technical Report A.I. Memo No. 1491, C.B.C.L. Paper No. 99, Massachusetts Institute of Technology, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Dep. of Brain and Cognitive Sciences, 1994.
7. D.A. Cohn, Z. Ghahramani, and M.I. Jordan, "Active learning with statistical models", in G. Tesauro, D. Touretzky, and T.K. Leen (eds) Advances in Neural Processing Systems, Vol. 7, pp. 705-712, MIT Pr., Cambridge, MA, 1995.
8. K. Fukumizu, "Active learning in multilayer perceptrons", in D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo (eds) Advances in Neural Information Processing Systems, Vol. 8, pp. 295-301, MIT Pr., Cambridge, 1996.
9. L.M. Belue, K.W. Bauer Jr., and D.W. Ruck, "Selecting optimal experiments for multiple output multilayer perceptrons", Neural Computation, Vol. 9, pp. 161-183, 1997.
10. A. Gersho and R.M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers: Boston, MA, 1991.
11. F. Aurenhammer, "Voronoi diagrams - a survey of a fundamental geometric data structure", ACM Computing Surveys, Vol. 23, pp. 345-405, 1991.
12. T. Kohonen, Self-Organizing Maps, Chap. 6, pp. 175-189, Springer: Berlin, 1995.
13. K.J. Lang and M.J. Witbrock, "Learning to tell two spirals apart", in D. Touretzky, G. Hinton, and T. Sejnowski (eds) Proceedings of the 1988 Connectionist Summer School, pp. 52-59, Morgan Kaufmann, San Mateo, CA, 1989.
14. C.B. Barber, D.P. Dobkin, and H. Huhdanpaa, "The quickhull algorithm for convex hulls", submitted to the ACM Transactions on Mathematical Software, (included with qhull which is available by anonymous ftp, ftp-host: geom.umn.edu, ftp-file: pub/software/qhull.tar.Z), 1995.