Statistical modeling in machine learning : concepts and applications /: concepts and applications. (2022)

• Record Type: Book
• Further Information: Note: Edited by Tilottama Goswami, Ganesh R. Sinha.
• Editors: Goswami, Tilottama; Sinha, G. R, 1975-
• Contents: 1. Introduction to Statistical Modelling in Machine Learning - A Case Study; 2. A Technique of Data Collection- Web Scraping with Python; 3. Analysis of Covid-19 using Machine Learning Techniques; 4. Discriminative Dictionary Learning based on Statistical Methods; 5. Artificial Intelligence based Uncertainty Quantification technique for External flow CFD simulations; 6. Music Genres Classification; 7. Classification Model of Machine Learning for Medical Data Analysis; 8. Regression Models for Machine learning; 9. Model Selection and Regularization; 10. Data Clustering using Unsupervised Machine Learning; 11. Emotion-based classification through fuzzy entropy enhanced FCM clustering; 12. Fundamental Optimization Methods for Machine Learning; 13. Stochastic Optimization of Industrial Grinding Operation through Data-Driven Robust Optimization; 14. Dimensionality Reduction using PCAs in Feature Partitioning Framework; 15. Impact of Mid-Day Meal Scheme in Primary Schools in India using Exploratory Data Analysis and Data Visualisation; 16. Nonlinear System Identification of Environmental pollutants using Recurrent Neural Networks and Global Sensitivity Analysis; 17. Comparative Study of Automated Deep Learning Techniques for Wind Time Series Forecasting
• Publisher Details: Amsterdam : Academic Press
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 006.31; Machine learning -- Statistical methods
• Languages: English
• ISBNs: 9780323972529
• Related ISBNs: 9780323917766
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.