Finding a tube of small width that covers a certain percentage of the training data samples is a robustway to estimate a location: the values of the data samples falling outside the tube have no direct influence on the estimate. The well-known ν-tube Support Vector Regression (ν-SVR) is an effective method for implementing this idea in the context of covariates. However, the ν-SVR considers onl...

Addressing the will to give a more complete picture than an average relationship provided by standard regression, a novel framework for estimating and predicting simultaneously several conditional quantiles is introduced. The proposed methodology leverages kernel-based multi-task learning to curb the embarrassing phenomenon of quantile crossing, with a one-step estimation procedure and no post-...

Nowadays Big Data are becoming more and more important. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. In such systems, capacity a...

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.

Generalized estimating equations (GEE; Liang & Zeger, 1986) are extended to general vector regression settings. When the response vectors are of mixed type (e.g. continuous–binary response pairs), the proposed method is a semiparametric alternative to full-likelihood copula methods. When the response vectors are of the same type (e.g. physical measurements on left and right eyes), the proposed ...

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 ...

Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to mo...