Chauhan V.K. Stochastic Optimization for Large-scale Machine Learning

CRC Press, 2022. — 177 p. — ISBN: 9781032131757.Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large-scale learning problems.Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimization techniques are also explored to improve machine learning algorithms based on data access and first and second-order optimization methods.Key FeaturesBridges machine learning and Optimization.
Bridges theory and practice in machine learning.
Identifies key research areas and recent research directions to solve large-scale machine learning problems.
Develops optimization techniques to improve machine learning algorithms for big data problems.Optimization Problem, Solvers, Challenges and Research Directions.
FIRST ORDER METHODS.
Mini-batch and Block-coordinate Approach.
Variance Reduction Methods.
Learning and Data Access.
SECOND ORDER METHODS.
Mini-batch Block-coordinate Newton Method.
Stochastic Trust Region Inexact Newton Method.Conclusion and Future Scope.