Do we need whatever more than k-NN?

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

Volumn 6113 LNAI, Issue PART 1, 2010, Pages 414-421

  Kordos, Mirosław ^a   Blachnik, Marcin ^b   Strzempa, Dawid ^a  

^a UNIVERSITY OF BIELSKO BIALA   (Poland)

^b SILESIAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; DATA SETS; DISTANCE MEASURE; GOLD STANDARDS; KD-TREE; NEAREST NEIGHBOR SEARCH STRATEGY; REFERENCE VECTORS; TEST TIME; TRAINING TIME;

ARTIFICIAL INTELLIGENCE; SOFT COMPUTING;

ALGORITHMS;

EID: 77955345584     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13208-7_52     Document Type: Conference Paper

References (22)

1. Fix, E., Hodges, J.L.: Discriminatory analysis, nonparametric discrimination: Consistency properties. USAF School of Aviation Medicine, Randolph Field, Texas (1951)
2. http://www.scholarpedia.org/article/K-nearest-neighbor
3. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml
4. Strzempa, D.: Internet System for Data Classification (in Polish), MSc Thesis, The Silesian University of Technology, Katowice (2008), http://www.ath.bielsko.pl/~mkordos/mgr/ds2008.pdf
5. Duda, R.O., et al.: Pattern Classification. Wisley, New York (2001)
6. Breiman, L., et al.: Classification and Regression Trees. Wadsworth, CA (1984)
7. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
8. Kohonen, T.: Statistical Pattern Recognition Revisited. Elsevier, Amsterdam (1990)
9. Schalkoff, R.: Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley, Chichester (1992)
10. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Wiley, Chichester (1950)
11. Kordos, M.: Search-based Algorithms for Multilayer Perceptrons, PhD Thesis, The Silesian University of Technology, Gliwice (2005), http://www.fizyka.umk.pl/~kordos/pdf/MKordos-PhD.pdf
12. Grabczewski, K.: Application of SSV Criterion for generating classification rules, PhD Thesis, Nicholaus Copernicus University, Torun (2003) (in Polish)
13. Jankowski, N.: Ontogenic Neural Networks for Medical Data Classification, PhD Thesis, Nicholaus Copernicus University, Torun (1999) (in Polish)
14. Adamczak, R.: Neural networks application for experimental data classification, PhD Thesis, Nicholaus Copernicus University, Torun (1999, 2001) (in Polish)
15. Schlkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge (2001)
16. Michie, D., Spiegelhalter, D.J., Taylor, C.C.:Machine Learning, neural and statistical classification. Elis Horwood, London (1994)
17. Ster, B., Dobnikar, A.: Neural Networks in medical diagnosis: Comparison with other methods. In: EANN 1996, pp. 427-430 (1996)
18. Zarndt, F.: A comprehensive case study: An examination of machine learning and connectionists algorithms, MSc Thesis, Department of Computer Science, Brigham Young University (1995)
19. Weiss, S.M., Kapouleas, I.: An empirical comparison of pattern Recognition, neural nets and machine learning classification methods. In: Reading in Machine Learning. Morgan Kauffman Publ., CA (1990)
20. http://www.fqs.pl/business-intelligence/products/ ghostminer/product- overview
21. http://www.is.umk.pl/projects/datasets.html
22. Blachnik, M., Duch, W., Wieczorek, T.: Probabilistic distance measures for prototype-based rules. In: Proc. of the 12th Int. Conference on Neural Information Processing (ICONIP 2005), Taipei, Taiwan, pp. 445-450 (2005)