Fast SVM training using approximate extreme points

Applications of non-linear kernel Support Vector Machines (SVMs) to large datasets is seriously hampered by its excessive training time. We propose a modification, called the approximate extreme points support vector machine (AESVM), that is aimed at overcoming this burden. Our approach relies on conducting the SVM optimization over a carefully selected subset, called the representative set, of the training dataset. We present analytical results that indicate the similarity of AESVM and SVM solutions. A linear time algorithm based on convex hulls and extreme points is used to compute the representative set in kernel space. Extensive computational experiments on nine datasets compared AESVM to LIBSVM \citep{LIBSVM}, CVM \citep{Tsang05}, BVM \citep{Tsang07}, LASVM \citep{Bordes05}, $\text{SVM}^{\text{perf}}$ \citep{Joachims09}, and the random features method \citep{rahimi07}. Our AESVM implementation was found to train much faster than the other methods, while its classification accuracy was similar to that of LIBSVM in all cases. In particular, for a seizure detection dataset, AESVM training was almost $10^3$ times faster than LIBSVM and LASVM and more than forty times faster than CVM and BVM. Additionally, AESVM also gave competitively fast classification times.Comment: The manuscript in revised form has been submitted to J. Machine Learning Researc

Sustainability in higher education for the global south a conversation across geographies and disciplines

A workshop on ‘Sustainability in Higher Education from the vantage of the Global South’ was organized by the Azim Premji University between 12 and 14 January 2015 in Bengaluru, India. Its goal was to explore how sustainability can be integrated into undergraduate, postgraduate and professional courses. The workshop was divided into four sessions with interlinked themes – the first, with a focus on framing sustainability; the second, on integrating sustainability in higher education; the third, on sustainability curricula; and the last, on pedagogy for sustainability. All four sessions were informed by the broader educational goal of enabling students from diverse backgrounds to envision, conceptualise, research and implement sustainability in varied personal and professional contexts. Participants of the workshop drew upon their varied experiences, from India and institutions across the world, in the teaching and learning of the multidimensional concept of sustainability in diverse geographies. The questions, counterquestions, discussions and potential solutions raised during the workshop are presented in this paper in a dialogic styl

Sustainability in higher education for the global south: A conversation across geographies and disciplines

A workshop on ‘Sustainability in Higher Education from the vantage of the Global South’ was organized by the Azim Premji University between 12 and 14 January 2015 in Bengaluru, India. Its goal was to explore how sustainability can be integrated into undergraduate, postgraduate and professional courses. The workshop was divided into four sessions with interlinked themes – the first, with a focus on framing sustainability; the second, on integrating sustainability in higher education; the third, on sustainability curricula; and the last, on pedagogy for sustainability. All four sessions were informed by the broader educational goal of enabling students from diverse backgrounds to envision, conceptualise, research and implement sustainability in varied personal and professional contexts. Participants of the workshop drew upon their varied experiences, from India and institutions across the world, in the teaching and learning of the multidimensional concept of sustainability in diverse geographies. The questions, counterquestions, discussions and potential solutions raised during the workshop are presented in this paper in a dialogic style

Fast SVM Training Using Approximate Extreme Points

Applications of non-linear kernel Support Vector Machines (SVMs) to large datasets is seriously hampered by its excessive training time. We propose a modification, called the approximate extreme points support vector machine (AESVM), that is aimed at overcoming this burden. Our approach relies on conducting the SVM optimization over a carefully selected subset, called the representative set, of the training dataset. We present analytical results that indicate the similarity of AESVM and SVM solutions. A linear time algorithm based on convex hulls and extreme points is used to compute the representative set in kernel space. Extensive computational experiments on nine datasets compared AESVM to LIBSVM \citep{LIBSVM}, CVM \citep{Tsang05}, BVM \citep{Tsang07}, LASVM \citep{Bordes05}, $\text{SVM}^{\text{perf}}$ \citep{Joachims09}, and the random features method \citep{rahimi07}. Our AESVM implementation was found to train much faster than the other methods, while its classification accuracy was similar to that of LIBSVM in all cases. In particular, for a seizure detection dataset, AESVM training was almost $10^3$ times faster than LIBSVM and LASVM and more than forty times faster than CVM and BVM. Additionally, AESVM also gave competitively fast classification times