In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

/pdf/a-tutorial-on-support-vector-regression-10mfvogjkf.pdf

A tutorial on support vector regression

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

/pdf/an-introduction-to-kernel-based-learning-algorithms-4blkbqa7z6.pdf

An introduction to kernel-based learning algorithms

We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.

https://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf

New Support Vector Algorithms

Introduction to linear and nonlinear programming

http://svms.org/parameters/CherkasskyMa2004.pdf

Practical selection of SVM parameters and noise estimation for SVM regression

Support vector machines for dynamic reconstruction of a chaotic system

Most Support Vector (SV) methods proposed in the recent literature can be viewed in a uni ed framework with great exibility in terms of the choice of the basis functions. We show that all these problems can be solved within a unique approach if we are equipped with a robust method for nding a sparse solution of a linear system. Moreover, for such a purpose, we propose an iterative algorithm that can be simply implemented. This allows us to generalize the classical SV method to a generic choice of the basis functions.

Generalized support vector machines.

The paper deals with the transmission over multiple-input/multiple-output channels exhibiting time-dispersion. A minimum mean-square error equalizer based on widely linear processing combined with the decision-feedback (DF) strategy is implemented via finite-impulse-response filters. The proposed equalizer provides considerable performance gain at the expense of a limited increase in computational complexity. The performance analysis has been carried out accounting for mismatch conditions always present in practice. The results confirm the stronger sensitivity of the DF-based equalizers with respect to the feedforward-based ones when system parameters are not accurately known.

Widely linear decision-feedback equalizer for time-dispersive linear MIMO channels

The paper deals with the problem of blind synchronization for OFDM/OQAM systems. Specifically, by exploiting the approximate conjugate-symmetry property of the beginning of a burst of OFDM/OQAM symbols, due to the presence of the time offset, a new procedure for blind symbol timing and CFO estimation is proposed. The performance of the derived blind estimators is analyzed by computer simulations; the results show that the proposed methods may provide acceptable performance for reasonable values of the signal-to-noise ratio.

Blind Symbol Timing and CFO Estimation for OFDM/OQAM Systems

We consider the problem of designing a sparse FIR filter and show that it can be cast into a problem of determining a sparse solution of a linear system of equations. Previously proposed design algorithms for FIR filter utilize an intelligent search over all possible structures for sparse filter. We propose a new filter design method based on a simpler algorithm for finding a sparse solution of the linear system. Simulation experiments show significant improvements over classical nonsparse methods.

Davide Mattera

Papers

Support vector machines for dynamic reconstruction of a chaotic system

Generalized support vector machines.

Widely linear decision-feedback equalizer for time-dispersive linear MIMO channels

Blind Symbol Timing and CFO Estimation for OFDM/OQAM Systems

Efficient sparse FIR filter design