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Bayesian reasoning and Gaussian processes for machine learning applications / edited by Hemachandran K., Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose

Publisher	(Boca Raton : Chapman & Hall/CRC Press)
Year	2022
Edition	First edition.
Authors	K., Hemachandran editor Tayal, Shubham editor George, Preetha Mary editor Singla, Parveen editor Kose, Utku 1985- editor

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Links to the text	Location	Volume	Call No.	Barcode No.	Status	Comments	ISBN	Printed	Restriction	Reserve
Links to the text	Library Off-campus access			OB00190632		Taylor & Francis eBooks (電子ブック)	9781003164265

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Material Type	E-Book
Media type	機械可読データファイル
Size	1 online resource
Notes	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 OCLC-licensed vendor bibliographic record HTTP:URL=https://www.taylorfrancis.com/books/9781003164265 Information=Taylor & Francis
Subjects	LCSH:Bayesian statistical decision theory -- Data processing All Subject Search LCSH:Gaussian processes -- Data processing All Subject Search LCSH:Machine learning FREE:BUSINESS & ECONOMICS / Statistics FREE:MATHEMATICS / Probability & Statistics / Bayesian Analysis FREE:COMPUTERS / Machine Theory
Classification	LCC:QA279.5 DC23:006.3/101519542
ID	8000089798
ISBN	9781003164265