The self-organizing feature maps

Kohonen, Teuvo; Mäkisara, Kai

doi:10.1088/0031-8949/39/1/027

Cited by 35 publications

(25 citation statements)

References 4 publications

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“…A high number indicated the effect of analgesia (anesthetic effect) was strongly present. A descriptive study of the data has been published in one of the pharmacological journals, Rattan (1991).…”

Section: Resultsmentioning

confidence: 99%

“…We still assume the time-modulated frailty model, and the frailty term is in the form of As we can see from A real data example The well-known Chronic Granulomatous Disease (CGD) dataset, which is described in the Appendix D of the book by Fleming and Harrington (1991), has been analyzed by many authors. CGD is a group of inherited rare disorders of the immune function characterized by recurrent pyogenic infections, which usually present early life and may lead to death in childhood.…”

Section: Simulationsmentioning

confidence: 99%

“…In order to study the ability of gamma interferon to reduce the rate of serious infections, a doubleblinded clinical trial was conducted in which patients were randomized to placebo vs. gamma interferon. The data we use here, which is a little different from the one used by Fleming and Harrington (1991) in the example at page 162, has 65 patients in placebo group, 63 in gamma interferon group, of 30 placebo patients who experienced at least one infection, 4 experienced 2, 4 experienced 3, 1 experienced 4, 1 experienced 5 and 1 experienced 7; of 14 treatment patients who experienced at least one infection, 4 experienced 2 and 1 experienced 3.…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

“…The tendency for AIC to select overly complex models in cases where complexity is known has been interpreted as a shortcoming of this measure. Hurvich and Tsai (1991), for example, argue for a modified version of AIC that incorporates sample size. In practical applications, however, the performance of criteria such as AIC and BIC can be quite complex.…”

Section: Introductionmentioning

confidence: 99%

“…Bozdogan (1987) compared rates of successful model identifications for AIC and CAIC (a close kin of BIC) for a single cubic model with various error structures. Hurvich and Tsai (1991) compared AIC and their own consistent estimator, AICC, for a normal regression case and for a complex time series. Bai et al (1992) compared AIC and several modifications of AIC within the context of multinomial logistic regression models.…”

Section: Introductionmentioning

confidence: 99%

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Vol. 1, No. 2 (Full Issue)

2002

J. Mod. App. Stat. Meth.

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Section: Resultsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: ------------------------------------------------------------mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vol. 1, No. 2 (Full Issue)

2002

J. Mod. App. Stat. Meth.

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Evaluation of machine learning algorithms to classify and map landforms in Antarctica

Siqueira

Veloso

Filho

et al. 2021

Earth Surf Processes Landf

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The detailed geomorphology of ice‐free landscapes of Antarctica is key to understanding how their highly fragile environments respond to climate change, at different temporal and spatial scales. Despite the recent advances in geomorphological studies of ice‐free areas, machine learning applications to produce landform maps are still scarce on the Antarctic continent. In this study, we evaluated the predictive performance of different supervised machine learning algorithms to produce digital geomorphological maps in Vega Island—Antarctic Peninsula region. We tested six different models: average artificial neural networks, C5.0 decision tree, random forest, support vector machine, supervised self‐organizing map and weighted k‐nearest neighbours. We used an initial set of 54 geomorphometric and spectral predictors, from which redundant variables with Pearson correlation coefficient >|0.95| were removed, and only the most important predictors for each model were selected using recursive feature elimination. For training, we ran each model 100 times and predictions were assessed by the kappa and global accuracy values. The best predictors were the Red Edge 6 and SWIR 11 bands, roughness concentration index, elevation and drainage density. The decision trees C5.0 and random forest had the best performance, with average validation kappa of 0.85 ± 0.03 and 0.84 ± 0.03, respectively, evidencing excellent prediction. Despite the similar performance, random forest showed greater uncertainty degree and accuracy when classifying complex landforms, attesting to its great robustness. From sensitivity and specificity values, we observed that the glaciers and talus showed higher accuracy, whereas cryoplanated platforms and scree slopes had the worst classification. The presented methodology optimized the classification by selecting the most important predictors, assessing accuracy and evaluating uncertainty. The results indicated that machine learning methods have great potential to produce geomorphological mappings in the Antarctic ice‐free areas, as a promising tool to provide detailed information on remote, harsh polar environments.

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A neural network model of competitive learning which converges to the global equilibrium

Masuda

1992

Systems & Computers in Japan

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SUMMARYWith regard to competitive learning a neural network model consisting of two layers of neurons was constructed and a new energy (loss) function to ensure that the network will converge uniquely to the desired state is proposed. It was proved that when the number of cells in the hidden layer is equal to the number of the input patterns, this model does not have any local minima of the energy function other than the one that yields the global minimum and, consequently, converges to the global minimum from any initial values of the synaptic connections.In this equilibrium the network is organized to the state where only a single hidden cell responds to its corresponding input pattern. This model is compared with the other models of competitive learning proposed up to the present. Then a model of the associative memory based on this competitive learning model is proposed. In this model of the associative memory only output patterns that are presented during the learning are recalled even in response to an input that has never been presented during the learning. This model has an extra advantage in that the input patterns are requested to be neither orthogonal nor independent of each other.

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The self-organizing feature maps

Cited by 35 publications

References 4 publications

Vol. 1, No. 2 (Full Issue)

Vol. 1, No. 2 (Full Issue)

Evaluation of machine learning algorithms to classify and map landforms in Antarctica

A neural network model of competitive learning which converges to the global equilibrium

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