Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. ...

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...

In this paper, we model large support vector machines (SVMs) by smaller networks in order to decrease the computational cost. The key idea is to generate additional training patterns using a trained SVM and use these additional patterns along with the original training patterns to train a neural network. Results verify the validity of the technique. Introduction A key element in a pattern recog...

this paper aims to assess the application of support vector machine (svm) regression in order to analysis flexible pavements. to this end, 10000 four-layer flexible pavement sections consisted of asphalt concrete layer, granular base layer, granular subbase layer, and subgrade soil were analyzed under the effect of standard axle loading using multi-layered elastic theory and pavement critical r...

Support vector machines (SVMs) are very popular methods for solving classification problems that require mapping input features to target labels. When dealing with real-world data sets, the different classes are usually not linearly separable, and therefore support vector machines employ a particular kernel function. Such a kernel function computes the similarity between two input patterns, but...

The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature extraction, is then utilized as a preprocessor to improve business failure prediction capability by ...

Se presenta la evaluación de la predicción de múltiples puntos de series de tiempo, mediante un corrimiento de ventana para Support Vector Machines (SVM) con dos funciones de kernel distintas (lineal y con base radial). Para la evaluación se utilizó un conjunto de treinta series de diferente origen y comportamiento dinámico. Se encuentra que la SVM posee una buena capacidad para ajustarse a las...

Twin support vector machines (TWSVM) is similar in spirit to proximal SVM based on generalized eigenvalues (GEPSVM), which constructs two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is only 1/4 of standard SVM. In addition to keeping the advantages of GEPSVM, the classification performance of TWSVM is also significantly better th...

In this paper, we have developed a robust Support Vector Machines (SVM) scheme of classifying imbalanced and noisy data using the principles of Robust Optimization. Uncertainty is prevalent in almost all datasets and has not been addressed efficiently by most data mining techniques, as these are based on deterministic mathematical tools. Imbalanced datasets exist while performing analysis of ra...

Support Vector Machines (SVM) has been shown to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear sc...