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Bayesian reasoning and Gaussian processes for machine learning applications / edited by Hemachandran K, [and four others].
Format
Book
Language
English
Εdition
First edition.
Published/Created
Boca Raton, Florida ; Abingdon, Oxon : CRC Press, [2022]
©2022
Description
1 online resource (xiv, 133 pages) : illustrations
Availability
Available Online
SCI-TECHnetBASE
Taylor & Francis eBooks Complete
Details
Subject(s)
Bayesian statistical decision theory
—
Data processing
[Browse]
Editor
K., Hemachandran
[Browse]
Summary note
"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.
Bibliographic references
Includes bibliographical references and index.
Source of description
Description based on print version record.
Contents
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.
Show 7 more Contents items
ISBN
1-00-316426-9
1-003-16426-9
1-000-56958-6
OCLC
1294399024
Doi
10.1201/9781003164265
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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Other versions
Bayesian reasoning and Gaussian processes for machine learning applications / edited by Hemachandran K, Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose.
id
99125530814106421