We study the connection between higher order total variation (TV) regularization and support vector regression (SVR) with spline kernels in a one-dimensional discrete setting. We prove that the contact problem arising in the tube formulation of the TV minimization problem is equivalent to the SVR problem. Since the SVR problem can be solved by standard quadratic programming methods this provide...

We develop an intuitive geometric framework for support vector regression (SVR). By examining when ǫ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard and soft ǫ-tubes are constructed by separating the convex or reduced convex hulls respectively of the training data with the response variable shifted up and down by ǫ. A novel SVR model is proposed...

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) ...

This paper describes the music emotion recognition system developed at the University of Aizu for the Emotion in Music task of the MediaEval’2013 benchmark evaluation campaign. A set of standard feature types provided by the Marsyas toolkit was used to parametrize each music clip. Arousal and valence are modeled separately using Gaussian Process regression (GPR). We compared performances of the...

In this paper, we describe an approach to estimate optic flow from an image sequence based on Support Vector Regression (SVR) machines with an adaptive e-margin. This approach uses affine and constant models for velocity vectors. Synthetic and real image sequences are used in order to compare results of the SVR approach against other well-known optic flow estimation methods. Experimental result...

The orthogonal least squares algorithm (OLS) and the support vector regression (SVR) are two popular approaches to choose the structure of the Radial Basis Function Network (RBFN). The former is derived based only on the modelling errors, whilst the latter also on the model complexity. A comparison of the generalization results of networks selected from the OLS and the SVR is presented here usi...

In this work, we propose to apply support vector regression (SVR) to build software reliability growth model (SRGM). SRGM is an important aspect in software reliability engineering. Software reliability is the probability that a given software will be functioning without failure during a specified period of time in a specified environment. In order to obtain the better performance of SRGM, prac...

Support vector regression (SVR) has been popular in the past decade, but it provides only an estimated target value instead of predictive probability intervals. Many work have addressed this issue but sometimes the SVR formula must be modified. This paper presents a rather simple and direct approach to construct such intervals. We assume that the conditional distribution of the target value dep...

Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process (GPR), and...

A hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) is proposed for predictive models in the environmental sciences. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Three environmental forecast datasets used in the WCCI-2006 contest – surface air temperature, precipitation and sulphur dioxide concentration – we...