A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.

An Equivalence Between Sparse Approximation and Support Vector Machines

In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, 1995) and Olshausen and Field (1996). Then we focus on two specific (approximate) implementations of SRM and Sparse Approximation, which have been used to solve the problem of function approximation. For SRM we consider the Support Vector Machine technique proposed by V. Vapnik and his team at AT&T Bell Labs, and for Sparse Approximation we consider a modification of the Basis Pursuit De-Noising algorithm proposed by Chen, Donoho and Saunders (1995). We show that, under certain conditions, these two techniques are equivalent: they give the same solution and they require the solution of the same quadratic programming problem.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

A Note on Support Vector Machines Degeneracy

When training Support Vector Machines (SVMs) over non-separable data sets, one sets the threshold $b$ using any dual cost coefficient that is strictly between the bounds of $0$ and $C$. We show that there exist SVM training problems with dual optimal solutions with all coefficients at bounds, but that all such problems are degenerate in the sense that the "optimal separating hyperplane" is given by ${f w} = {f 0}$, and the resulting (degenerate) SVM will classify all future points identically (to the class that supplies more training data). We also derive necessary and sufficient conditions on the input data for this to occur. Finally, we show that an SVM training problem can always be made degenerate by the addition of a single data point belonging to a certain unboundedspolyhedron, which we characterize in terms of its extreme points and rays.

Interacting software-agents to support a kind of expert supervision systems

Expert supervision systems are software applications specially designed to automate process monitoring. The goal is to reduce the dependency on human operators to assure the correct operation of a process including faulty situations. Construction of this kind of application involves an important task of design and development in order to represent and to manipulate process data and behaviour at different degrees of abstraction for interfacing with data acquisition systems connected to the process. This is an open problem that becomes more complex with the number of variables, parameters and relations to account for the complexity of the process. Multiple specialised modules tuned to solve simpler tasks that operate under a co-ordination provide a solution. A modular architecture based on concepts of software agents, taking advantage of the integration of diverse knowledge-based techniques, is proposed for this purpose. The components (software agents, communication mechanisms and perception/action mechanisms) are based on ICa (Intelligent Control architecture), software middleware supporting the build-up of applications with software agent features

The UK Professional Standards Framework: applying it to support for international students

This is a part of a collection of materials developed by the HEAcademy Subject Centre for Languages, linguistics and area studies. The materials provide reflective activities designed to engage teachers with some of the key issues in working with international students and practical ideas for ways in which these can be addressed. They will be of particular interest to new staff or anyone new to working with international students.

The UK Professional Standards Framework: applying it to support for international students

This is a part of a collection of materials developed by the HEAcademy Subject Centre for Languages, linguistics and area studies. The materials provide reflective activities designed to engage teachers with some of the key issues in working with international students and practical ideas for ways in which these can be addressed. They will be of particular interest to new staff or anyone new to working with international students.

The UK Professional Standards Framework: applying it to support for international students

This is a part of a collection of materials developed by the HEAcademy Subject Centre for Languages, linguistics and area studies. The materials provide reflective activities designed to engage teachers with some of the key issues in working with international students and practical ideas for ways in which these can be addressed. They will be of particular interest to new staff or anyone new to working with international students.

The UK Professional Standards Framework: applying it to support for international students

This is a part of a collection of materials developed by the HEAcademy Subject Centre for Languages, linguistics and area studies. The materials provide reflective activities designed to engage teachers with some of the key issues in working with international students and practical ideas for ways in which these can be addressed. They will be of particular interest to new staff or anyone new to working with international students.

Support for International Students

International student experience video - this video is 20 minutes long and has been devised by Bill Brooks, Hazel Chauhan and Karin Frydenlund, with support from the University's Learning and Teaching Enhancement Fund. It includes contributions from a number of international students and from members of staff with particular expertise in this area.

Materials for generic student support

This is a group of items which can provide additional support to students with special requirements.

Online Practicals to Support Labs - Matthew Bellamy

This is one of a series of short case studies describing how academic tutors at the University of Southampton have made use of learning technologies to support their students.

Using wikis to support a field trip - Joel Parker

This is one of a series of short case studies describing how academic tutors at the University of Southampton have made use of learning technologies to support their students.

Pearson e-book to support students - Patrick Doncaster

This is one of a series of short case studies describing how academic tutors at the University of Southampton have made use of learning technologies to support their students.

Using video markschemes to support self-assessment in Chemistry

A video markscheme was created using a combination of Camtasia screen capture (on a Tablet PC) and 'live action' video taken with a camcorder. The resulting video supported students in the self-assessment of an organic chemistry exercise which had been set over the Easter vacation break. Feedback was collected from the students after the exercise and was overwhelmingly positive. The video won the 2010 award for 'Most Effective Use of Video in an Educational Context' from the Assocation for Learning Technology. DOWNLOAD THE ZIP FOLDER AND EXTRACT THE FILES TO ACCESS THEM.