Hemachandran K; Raul V. Rodriguez; Umashankar Subramaniam; Valentina Emilia Balas Taylor & Francis Ltd (2023) Saatavuus: Tilaustuote Kovakantinen kirja
Hemachandran K; Manjeet Rege; Zita Zoltay Paprika; K. V. Rajesh Kumar; Shahid Mohammad Ganie Taylor & Francis Ltd (2024) Saatavuus: Tilaustuote Kovakantinen kirja
Hemachandran K; Debdutta Choudhury; Raul Villamarin Rodriguez; Jorge A. Wise; Revathi T Taylor & Francis Ltd (2024) Saatavuus: Tulossa! Kovakantinen kirja
Hemachandran K (ed.); Raul Villamarin Rodriguez (ed.); Manjeet Rege (ed.); Vincenzo Piuri (ed.); Guandong Xu (ed.); Ko Ong Springer (2024) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Syed Hasan Jafar; Hemachandran K; Shakeb Akhtar; Parvez Alam Khan; Hani El-Chaarani Taylor & Francis Ltd (2024) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Syed Hasan Jafar; Hemachandran K; Shakeb Akhtar; Parvez Alam Khan; Hani El-Chaarani Taylor & Francis Ltd (2024) Saatavuus: Tilaustuote Kovakantinen kirja
Taylor & Francis Ltd Sivumäärä: 133 sivua Asu: Kovakantinen kirja Julkaisuvuosi: 2022, 14.04.2022 (lisätietoa) Kieli: Englanti
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models.
FEATURES
Contains recent advancements in machine learning
Highlights applications of machine learning algorithms
Offers both quantitative and qualitative research
Includes numerous case studies
This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.