Calibration of self-organizing maps for classification tasks

Bach, Carme; Bredl, S.; Kossa, W.; Sick, Beate

doi:10.1109/icsmc.2003.1244328

Cited by 1 publication

(4 citation statements)

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“…The calibration algorithms one-point algorithm and many-points algorithm presented in [23] are based on the assumption that these clusters can be modeled by means of multivariate Gaussian distributions. The unknown parameters of the distributions and the assignment of output neurons to classes (i.e., an appropriate partition of the output neurons) are determined using a maximum likelihood (ML) estimation of the distribution parameters.…”

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

“…The four combinations of calibration algorithms and shape criteria are evaluated in [23] using the ''PenDigits'' data set (classification of handwritten digits) in order to show their main properties (termination and decrease with respect to the chosen criterion). In [36], the algorithms and criteria addressed in [23] are compared to two hierarchical clustering algorithms (single linkage and average linkage).…”

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

“…In [36], the algorithms and criteria addressed in [23] are compared to two hierarchical clustering algorithms (single linkage and average linkage). The ''Cancer'' data set (classification of breast cancer) and the ''Iris'' data set (classification of flowers) are used for this purpose.…”

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

“…The central idea of the fusion technique introduced in [23] is that an HC component (clustering algorithm based on statistical assumptions about the shape of clusters) is used to calibrate the training result of an SC component (SOM utilized for classification tasks).…”

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

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Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

Sick

Ovaska

2006

Neural Comput & Applic

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The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.

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Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

Section: Calibration Of Self-organizing Maps For Classificationmentioning

confidence: 99%

See 2 more Smart Citations