By Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel (auth.), Jorma Laaksonen, Timo Honkela (eds.)
This publication constitutes the refereed lawsuits of the eighth foreign Workshop on Self-Organizing Maps, WSOM 2011, held in Espoo, Finland, in June 2011. The 36 revised complete papers offered have been conscientiously reviewed and chosen from quite a few submissions. The papers are prepared in topical sections on plenaries; monetary and societal functions; idea and method; purposes of information mining and research; language processing and rfile research; and visualization and picture processing.
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Additional info for Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings
2} give the best results; higher fuzzy exponents give non-smooth memberships, while lower give roughly crisp memberships. 0 provided the best visual interpretability. The c-value was first set as 4 (Fig. 6), in accordance with the originally identified number of clusters on the map, but later adjusted to 3 (Fig. 7). The concern with the 4-cluster model is that the cluster termed Early warning does not directly contribute to the currency crisis cycle. Although it would, of course, be informative to have an Early warning cluster, the cluster is quite small and borders the pre-crisis cluster both between the tranquil cluster (as desired) and the crisis cluster (as not desired).
In earlier works the dimensionalities of all of the random-code vectors of the words were always taken as equal. A new idea in this work was to select the dimensionality as a function of the relative position of the word within the local context. The dimensionality of the target vector ri was selected as 50. The dimensionalities of ri−1 and ri+1 were taken equal to 200, and those of ri−2 and ri+2 equal to 100, respectively. In this way, the diﬀerent words within the context have diﬀerent statistical weights in the matching of the input vector with the SOM weight vectors.
5. Histograms of general nouns, as a function of word frequency Contextually Self-Organized Maps of Chinese Words 23 Fig. 6. Histograms of verbs without objects, as a function of word frequency 24 T. Kohonen and H. Xing Fig. 7. Histograms of verbs followed by nouns, as a function of word frequency Contextually Self-Organized Maps of Chinese Words 25 Fig. 8. Histograms of general adverbs, as a function of word frequency 26 T. Kohonen and H. Xing Contextually Self-Organized Maps of Chinese Words 27 The other verbs without objects are clustered mainly in the middle, where the nouns have an empty space.
Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings by Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel (auth.), Jorma Laaksonen, Timo Honkela (eds.)