Machine Learning: A Bayesian and Optimization Perspective - Sergios Theodoridis
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Toimitus 31-37 arkipäivässä
30 päivän palautusoikeus
Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling Provides case studies on a va ... Täydellinen kuvaus
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Kuvaus
Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more
Lisätietoja
| Kirjoittaja | Sergios Theodoridis |
|---|---|
| Julkaisija | Elsevier Science |
| Julkaisuvuosi | 2020 |
| Kannen tyyppi | Kovakantinen |
| EAN | 9780128188033 |