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Semantic Mapping (Statistics): Dimensionality Reduction, Clustering, Cluster, Data Set, Data Element, Text Mining, Information Retrieval -

Anglų
2026-03-14
138,79 € 213,53 €

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High Quality Content by WIKIPEDIA articles! The semantic mapping (SM) is a dimensionality reduction method that extracts new features by clustering the original features in semantic clusters and combining features mapped in the same cluster to generate an extracted feature. Given a data set, this method construct a projection matrix that can be used to mapping of data elements from one high dimensional spac ... Visas aprašymas

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High Quality Content by WIKIPEDIA articles! The semantic mapping (SM) is a dimensionality reduction method that extracts new features by clustering the original features in semantic clusters and combining features mapped in the same cluster to generate an extracted feature. Given a data set, this method construct a projection matrix that can be used to mapping of data elements from one high dimensional space into reduced dimensional space. The SM can be applied in construction of text mining and information retrieval systems, as well as systems managing vectors of high dimensionality. The SM is an alternative to principal components analysis and latent semantic indexing methods.

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Leidėjas OmniScriptum
Išleidimo metai 2026
Viršelio tipas Minkšti viršeliai
EAN 9786130491451
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Jūs peržiūrėjote: Semantic Mapping (Statistics): Dimensionality Reduction, Clustering, Cluster, Data Set, Data Element, Text Mining, Information Retrieval
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138,79 € 213,53 €