Machine Learning Models and Algorithms for Big Data Classification Thinking with Examples for Effective Learning /

• Κύριος συγγραφέας: Suthaharan, Shan (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US : Imprint: Springer, 2016.
• Έκδοση: 1st ed. 2016.
• Σειρά: Integrated Series in Information Systems, 36
• Θέματα: Business.; Management.; Database management.; Artificial intelligence.; Business and Management.; Database Management.; Artificial Intelligence (incl. Robotics).

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable...

Πίνακας περιεχομένων:

• Science of Information
• Part I Understanding Big Data
• Big Data Essentials
• Big Data Analytics
• Part II Understanding Big Data Systems
• Distributed File System
• MapReduce Programming Platform
• Part III Understanding Machine Learning
• Modeling and Algorithms
• Supervised Learning Models
• Supervised Learning Algorithms
• Support Vector Machine
• Decision Tree Learning
• Part IV Understanding Scaling-Up Machine Learning
• Random Forest Learning
• Deep Learning Models
• Chandelier Decision Tree
• Dimensionality Reduction.