03976cam a22004458i 4500 22390500 CSPC 20250320110046.0 220108s2022 flu b 001 0 eng 2021052540 9780367758479 (hardback) LBSOR/DLC eng rda DLC CSPC pcc QA279.5 .B43 2022 006.3101519542 B341 23/eng/20220128 Bayesian reasoning and Gaussian processes for machine learning applications / edited by Hemachandran K., [and 4 others]. First edition. 2204 Boca Raton, FL ; Abingdon, Oxon : Chapman & Hall/CRC Press, 2022. xiv, 133 pages ; 26 cm. text txt rdacontent unmediated n rdamedia volume nc rdacarrier Includes bibliographical references and index. Introduction to naive Bayes and a review on its subtypes with applications / Eguturi Manjith Kumar Reddy, Akash Gurrala, Vasireddy Bindu Hasitha, Korupalli V. Rajesh Kumar -- A review on different regression analysis in supervised learning / K. Sudhaman, Mahesh Akuthota and Sandip Kumar Chaurasiya -- Methods to predict the performance analysis of various machine learning algorithms / M. Saritha, M. Lavanya and M. Narendra Reddy -- A viewpoint on belief networks and their applications / G.S. Sivakumar, P. Suneetha, V. Sailaja and Pokala Pranay Kumar -- Reinforcement learning using Bayesian algorithms with applications / H. Raghupathi, G. Ravi and Rajan Maduri -- Alerting system for gas leakage in pipeline / Nilesh Deotale, Pragya Chandra, Prathamesh Dherange, Pratiksha Repaswal, Saibaba V. More -- New non-parametric models for biological networks / Deniz Seçilmiş, Melih Ağraz, Vilda Purutçuoğlu -- Generating various types of graphical models via MARS / Ezgi Ayyıldız and Vilda Purutçuoğlu -- Financial applications of Gaussian processes and Bayesian optimization / Syed Hasan Jafar -- Bayesian network inference on diabetes risk prediction data / Mustafa Özgür Cingiz. "The book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications talks about Bayesian Reasoning and Gaussian Processes in 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 for handling missing data and for extracting information from small datasets. This book introduces a statistical background which is needed to understand continuous distributions and it gives an understanding on how learning can be viewed from a probabilistic framework. The chapters of the book progress into machine learning topics such as Belief Network, Bayesian Reinforcement Learning etc., which is followed by Gaussian Process Introduction, Classification, Regression, Covariance and Performance Analysis of GP with other models. This book is aimed primarily at graduates, researchers and professionals in the field of data science and machine learning"-- Provided by publisher. Bayesian statistical decision theory Data processing. Gaussian processes Data processing. Machine learning. K., Hemachandran, editor. Tayal, Shubham, editor. George, Preetha Mary, editor. Singla, Parveen, editor. Kose, Utku, 1985- editor. Online version: Bayesian reasoning and Gaussian processes for machine learning applications. First edition Boca Raton : Chapman & Hall/CRC Press, 2022 9781003164265 (DLC) 2021052541 7 cbc orignew 1 ecip 20 y-gencatlg ddc BK 23 006.3101519542 B341 CIR 2022 0 0 0 ddc 0 0 CSPC CSPC CIR 2025-03-20 0 CIR 006.3101519542 B341 2022 029288 2025-03-20 11:01:08 1-1 2025-03-20 BK 29210 29210