Introduction on Support Vector Regression

Wissenschaftliche Studie , 2015 , 7 Seiten

Informatik - Sonstiges

Through this paper I wish to to give an introduction about support vector regression and also its various modes of usage.We will be seeing how this support vector regression is formulated and how its varies alternatives are derived.

Inhaltsverzeichnis (Table of Contents)

• Introduction
• Support Vector Regression
• The need of a flat function
• A Brief Introduction of Emperical Risk Minimisation
• ε-Support Vector Regression(ε-SVR)
• To compute b
• v- Support Vector Regression
• Conclusion
• Literaturverzeichnis (Bibliography)

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This paper aims to provide an introduction to Support Vector Regression (SVR) and its various applications. It explores the formulation of SVR and its different variations, highlighting the importance of finding a flat function that balances complexity and training errors. The paper also delves into the concept of empirical risk minimization and its role in achieving good generalization.

• Support Vector Regression (SVR)
• Flatness of a function
• Empirical Risk Minimisation
• ε-Support Vector Regression (ε-SVR)
• v-Support Vector Regression

Zusammenfassung der Kapitel (Chapter Summaries)

The introduction provides an overview of Support Vector Regression (SVR) and its applications in forecasting trends, such as stock market fluctuations and time series prediction. It also introduces key concepts like primal and dual variables and their role in optimization problems.

The chapter on Support Vector Regression delves into the core of the method, explaining how a function is derived to connect data points represented as samples. It emphasizes the importance of finding a flat function that minimizes complexity and training errors. The chapter also introduces the concept of empirical risk minimization and its role in achieving good generalization.

The chapter on ε-Support Vector Regression (ε-SVR) presents a modified approach to SVR. It introduces the concept of an ε-insensitive tube, which ignores errors within a certain width and focuses on minimizing errors outside this tube. The chapter explains how the ε-SVR function is formulated and how the dual optimization problem is solved.

The chapter on v-Support Vector Regression introduces another variation of SVR, where the width of the ε-insensitive tube is itself a variable. This approach allows for greater flexibility in adjusting the tube's width and improving accuracy.

Schlüsselwörter (Keywords)

The keywords and focus themes of the text include Support Vector Regression, flatness of a function, empirical risk minimization, ε-SVR, v-SVR, dual optimization, Lagrangian multipliers, and generalization.

• Autor: Vishnu Sudheer Menon (Autor:in)
• Erscheinungsjahr: 2015
• Seiten: 7
• Katalognummer: V294791
• ISBN (eBook): 9783656930167
• ISBN (Buch): 9783656930174
• Sprache: Englisch
• Schlagworte: introduction, support, vector, regression