Stochastic Optimization for Large-Scale Machine Learning

Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.

Info autore

Dr. Vinod Kumar Chauhan is a Research Associate in Industrial Machine Learning in the Institute for Manufacturing, Department of Engineering at University of Cambridge UK. He has a PhD in Machine Learning from Panjab University Chandigarh India. His research interests are in Machine Learning, Optimization and Network Science. He specializes in solving large-scale optimization problems in Machine Learning, handwriting recognition, flight delay propagation in airlines, robustness and nestedness in complex networks and supply chain design using mathematical programming, genetic algorithms and reinforcement learning.

Riassunto

Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.

Dettagli sul prodotto

• Autori: Vinod Kumar Chauhan
• Editore: Taylor & Francis Ltd.
• Contenuto: Libro
• Forma del prodotto: Tascabile
• Data pubblicazione: 07.10.2024
• Categoria: Scienze sociali, diritto, economia > Economia > Tematiche generali, enciclopedie; Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Comunicazione dati, reti
• EAN: 9781032146140
• ISBN: 978-1-0-3214614-0
• Numero di pagine: 158
• Categorie: CD, CS, Saga, machine learning, Optimization, COMPUTERS / Computer Science, MATHEMATICS / Optimization, BUSINESS & ECONOMICS / Operations Research, TECHNOLOGY & ENGINEERING / Electronics / General, MATHEMATICS / History & Philosophy, COMPUTERS / Programming / Games, COMPUTERS / Machine Theory, computer science, Mathematical theory of computation, Operational research, Mathematical foundations, Electronics engineering, Games development & programming, Games development and programming, COMPUTERS / Data Science / Machine Learning, Newton method, inexact Newton method, Variance reduction techniques, variance reduction, machine learning;stochasitc optimization;Big Data;algorithms, TRON Method, Soft Margin SVM, trust region methods, scalable optimization for big data analytics, second order methods, coordinate descent algorithms, parallel computing in AI, Constant Step Size, Data Access Time, Subproblem Solver, Stochastic Approximation Approach, Trust Region Subproblem, Linear SVM, SVM Problem, Trust Region Radius, Multi-class Data, Big Data Problems, Non-linear SVMs, L2 Loss Function, Fuzzy SVM, Hessian Vector Products, Proximal SVM, Backtracking Line Search