Search Results - subject:"unsupervised"

Self-organizing maps and its applications in sleep apnea research and molecular genetics

Guimaraes, Gabriela; Urfer, Wolfgang - 2000

This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains … type of applications. Furthermore, an integration of unsupervised neural networks with hidden markov models is proposed. …

Persistent link: https://www.econbiz.de/10010316650

Self-organizing maps and its applications in sleep apnea research and molecular genetics

Guimaraes, Gabriela; Urfer, Wolfgang - Institut für Wirtschafts- und Sozialstatistik, … - 2000

Persistent link: https://www.econbiz.de/10010955389

Self-organizing maps and its applications in sleep apnea research and molecular genetics

Guimaraes, Gabriela; Urfer, Wolfgang - 2000

Persistent link: https://www.econbiz.de/10009783555

Simulating processes of concept formation and communication

Honkela, Timo; Kononen, Ville; Lindh-Knuutila, Tiina; … - In: Journal of Economic Methodology 15 (2008) 3, pp. 245-259

language and communication in that process is. We consider, in particular, the role of unsupervised learning in the formation …

Persistent link: https://www.econbiz.de/10005141349

Bayesian spatial modeling of genetic population structure

Corander, Jukka; Sirén, Jukka; Arjas, Elja - In: Computational Statistics 23 (2008) 1, pp. 111-129

Persistent link: https://www.econbiz.de/10005184301

T-BAPS: A Bayesian Statistical Tool for Comparison of Microbial Communities Using Terminal-restriction Fragment Length Polymorphism (T-RFLP) Data

Tang, Jing; Tao, Jinglun; Urakawa, Hidetoshi; Corander, … - In: Statistical Applications in Genetics and Molecular Biology 6 (2007) 1, pp. 30-30

The investigation of microbial communities is an essential part of the study of the biosphere. Flexible molecular fingerprinting tools such as terminal-restriction fragment length polymorphism (T-RFLP) analysis are often applied in the studies to enable the characterization of the microbial...

Persistent link: https://www.econbiz.de/10005178551

EFFICIENT UNSUPERVISED MINING FROM NOISY CO-OCCURRENCE DATA

MAMITSUKA, HIROSHI - In: New Mathematics and Natural Computation (NMNC) 01 (2005) 01, pp. 173-193

We consider the problem of mining from noisy unsupervised data sets. The data point we call noise is an outlier in the … unsupervised data. We propose a new iterative sampling approach for this problem, using both model-based clustering and the …

Persistent link: https://www.econbiz.de/10004977609

Conceptual mapping through keyword coupled clustering

Marx, Zvika; Dagan, Ido - In: Mind and Society: Cognitive Studies in Economics and … 2 (2001) 2, pp. 59-85

Persistent link: https://www.econbiz.de/10005040731

Knowledge Discovery fromMixed Data by Artificial Neural Network with Unsupervised Learning

Hsu, Chung-Chian; Kung, Chien-Hao - ToKnowPress

the setting of unsupervised learning so that neither class attribute nor domain expert is required. Experimental results …

Persistent link: https://www.econbiz.de/10010684183