Machine Learning: A Bayesian and Optimization Perspective - Sergios Theodoridis
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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 ... Pilns apraksts
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Aprašymas
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
Vairāk informācijas
| Autors | Sergios Theodoridis |
|---|---|
| Izdevējs | Elsevier Science |
| Izlaides gads | 2020 |
| Vāka tips | Cietais vāks |
| EAN | 9780128188033 |