\pawanchapter{Online learning}

\section{The Support Vector Machine (SVM)}

% core tool used throughout this thesis is the SVM, so we summarise here

Learning forms a core component of the work presented in this thesis, and in all cases the tool we use is the Support Vector Machine (SVM) [cite], along with its extensions to structured output spaces [cite]. The SVM has been extensively studied over the past ?? years, and is widely used in machine learning and computer vision. It has consistently been shown to provide robust and reliable results, and with recent extensions has been adapted to be suitable for online learning. As such the SVM provides a solid foundation for the learning which we use in this thesis.


\section{Classification}




% standard setting for SVM is classification, derive it here

% hard margin
% soft margin
% kernel trick
% optimisation in primal or dual
% extension to multi-class classification?

\section{Structured prediction}

% extension of SVM to structured output spaces

\section{Online learning}

% since optimization problem is convex, suitable to use gradient descent. following research in neural nets, can also use stochastic gradient descent. more approximate solution, but enables us to make use of far more data -- better solution overall.