Kód: 35826629
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the proble ... celý popis
Angličtina
5607 Kč
Dostupnost:
50 % šance
Máme informaci, že by titul mohl být dostupný. Na základě vaší objednávky se ho pokusíme do 6 týdnů zajistit.
Zadejte do formuláře e-mailovou adresu a jakmile knihu naskladníme, zašleme vám o tom zprávu. Pohlídáme vše za vás.
Nákupem získáte 561 bodů
Anotace knihy
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.
FEATURES
This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
Parametry knihy
5607 Kč
Angličtina
Osobní odběr Praha, Brno a 47484 dalších
Copyright ©2008-26 nejlevnejsi-knihy.cz Všechna práva vyhrazenaSoukromíCookies
Vrácení do měsíce
571 999 099 (8-15.30h)Nákupní košík ( prázdný )