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...

We consider a regression problem where uncertainty affects to the dependent variable of the elements of the database. A model based on the standard -Support Vector Regression approach is given, where two hyperplanes need to be constructed to predict the interval-valued dependent variable. By using the Hausdorff distance to measure the error between predicted and real intervals, a convex quadrat...

Abstract: Training a SVR (support vector regression) requires the solution of a very large QP (quadratic programming) optimization problem. Despite the fact that this type of problem is well understood, the existing training algorithms are very complex and slow. In order to solve these problems, this paper firstly introduces a new way to make SVR have the similar mathematic form as that of a su...

Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consi...

In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain support vector machines that learn to extract relevant features from the input patterns or from the...

We present a novel algorithm for sparse online greedy kernelbased nonlinear regression. This algorithm improves current approaches to kernel-based regression in two aspects. First, it operates online at each time step it observes a single new input sample, performs an update and discards it. Second, the solution maintained is extremely sparse. This is achieved by an explicit greedy sparsificati...

This paper describes an on-line method for building ε-insensitive support vector machines for regression as described in (Vapnik, 1995). The method is an extension of the method developed by (Cauwenberghs & Poggio, 2000) for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like...

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...

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...