We investigate the problem of predicting variables of ordinal scale. This taks is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. The approach presented is ba...

Most multi-document summarization systems follow the extractive framework based on various features. While more and more sophisticated features are designed, the reasonable combination of features becomes a challenge. Usually the features are combined by a linear function whose weights are tuned manually. In this task, Support Vector Regression (SVR) model is used for automatically combining th...

A new algorithm for Support Vector regression is proposed. For a priori chosen , it automatically adjusts a exible tube of minimal radius to the data such that at most a fraction of the data points lie outside. The algorithm is analysed theoretically and experimentally.

Twin support vector regression (TSVR), as an effective regression machine, solves a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in the classical support vector regression (SVR), which makes the learning speed of TSVR approximately 4 times faster than that of the SVR. However, the empirical risk minimization principle is implemented in TSVR, whic...

Response modeling, which predicts whether each customer will respond or how much each customer will spend based on the database of customers, becomes a key factor of direct marketing. In previous researches, several classification approaches, include Support Vector Machines (SVM) and Neural Networks (NN), have been applied for response modeling. However, there are two drawbacks of conventional ...

This paper proposes a model for learning soft-monotonic regression functions in the presence of imperfect domain knowledge. It proposes an extension to support vector regression (SVR) wherein a new hardness parameter is introduced to configure the degree of monotonicity. The model supports multiple monotonicity constraints over multiple input dimensions simultaneously. The proposed model has be...

In this work, a regression problem is studied where the elements of the database are sets with certain geometrical properties. In particular, our model can be applied to handle data affected by some kind of noise or uncertainty and interval-valued data, and databases with missing values as well. The proposed formulation is based on the standard -Support Vector Regression approach. In the interv...

We introduce a new methodology, called SVORIM+, for utilizing privileged information of the training examples, unavailable in the test regime, to improve generalization performance in ordinal regression. The privileged information is incorporated during the training by modelling the slacks through correcting functions for each of the parallel hyperplanes separating the ordered classes. The expe...

The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression. Vapnik’s convex risk minimization principle [Vap98] has a dominating role in statistical machine learning theory. Important special ...

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the mini...