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Full Description
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.
Contents
1. Introduction to Naive Bayes and a Review on Its Subtypes with Applications 2. A Review on the Different Regression Analysis in Supervised Learning 3. Methods to Predict the Performance Analysis of Various Machine Learning Algorithms 4. A Viewpoint on Belief Networks and Their Applications 5. Reinforcement Learning Using Bayesian Algorithms with Applications 6. Alerting System for Gas Leakage in Pipelines 7. Two New Nonparametric Models for Biological Networks 8. Generating Various Types of Graphical Models via MARS 9. Financial Applications of Gaussian Processes and Bayesian Optimization 10. Bayesian Network Inference on Diabetes Risk Prediction Data
