Local metric adaptation for soft nearest prototype classification to classify proteomic data

被引:0
作者
Schleif, FM [1 ]
Villmann, T
Hammer, B
机构
[1] Univ Leipzig, Dept Math & Comp Sci, D-7010 Leipzig, Germany
[2] Univ Leipzig, Clin Psychotherapy, D-7010 Leipzig, Germany
[3] Bruker Dalton GmbH, D-04318 Leipzig, Germany
[4] Clausthal Univ Technol, Dept Comp Sci, Clausthal Zellerfeld, Germany
来源
FUZZY LOGIC AND APPLICATIONS | 2006年 / 3849卷
关键词
classification; learning vector quantization; metric adaptation; mass spectrometry; proteomic profiling;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a new method for the construction of nearest prototype classifiers which is based on a Gaussian mixture approach interpreted as an annealed version of Learning Vector Quantization. Thereby we allow the adaptation of the underling metric which is useful in proteomic research. The algorithm performs a gradient descent on a cost function adapted from soft nearest prototype classification. We investigate the properties of the algorithm and assess its performance on two clinical cancer data sets. Results show that the algorithm performs reliable with respect to alternative state of the art classifiers.
引用
收藏
页码:290 / 296
页数:7
相关论文
共 13 条
  • [1] Adam BL, 2002, CANCER RES, V62, P3609
  • [2] [Anonymous], 1997, SPRINGER SERIES INFO
  • [3] Mass spectrometry-based proteomics: Current status and potential use in clinical chemistry
    Binz, PA
    Hochstrasser, DF
    Appel, RD
    [J]. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 2003, 41 (12) : 1540 - 1551
  • [4] BLAKE C, 1998, UCI REP MACH LEARN D
  • [5] CRAMMER K, 2002, P NIPS 2002
  • [6] Supervised neural gas with general similarity measure
    Hammer, B
    Strickert, M
    Villmann, T
    [J]. NEURAL PROCESSING LETTERS, 2005, 21 (01) : 21 - 44
  • [7] Generalized relevance learning vector quantization
    Hammer, B
    Villmann, T
    [J]. NEURAL NETWORKS, 2002, 15 (8-9) : 1059 - 1068
  • [8] HAMMER B, 2003, P EUR S ART NEUR NET, P59
  • [9] HANNOVER IS, 2004, INTERNAL RESULTS LEU
  • [10] Self-organized formation of various invariant-feature filters in the adaptive-subspace SOM
    Kohonen, T
    Kaski, S
    Lappalainen, H
    [J]. NEURAL COMPUTATION, 1997, 9 (06) : 1321 - 1344