Training machine learning models sometimes needs to be done on large amounts of data that exceed the capacity of a single machine, motivating recent works on developing algorithms that train in a distributed fashion. This paper proposes an efficient box-constrained quadratic optimization algorithm for distributedly training linear support vector machines (SVMs) with large data. Our key technica...

Support Vector Machines have been successfully used in a number of applications such as the recognition of digits in postal codes and face recognition. However, their conventional implementation involves the use of quadratic programming techniques which are slow and not easy to implement. We outline a simple learning procedure for nd-ing the same maximal margin hyperplane in the feature space f...

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...

Airspace capacity estimates are important for managing air traffic and predicting the effectiveness of new airspace designs and proposed decision support tools. Because air traffic management relies on manual procedures, controller workload determines the traffic limit of most sectors. Current operational procedures for estimating capacity in United States airspace do not account for conflict a...

Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data ...

We present a simple technique for learning better SVMs using fewer training examples. Rather than using the standard SVM regularization, we regularize toward low weight-variance. Our new SVM objective remains a convex quadratic function of the weights, and is therefore computationally no harder to optimize than a standard SVM. Variance regularization is shown to enable dramatic improvements in ...

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...

In this paper, we study the incorporation of the support vector machine (SVM) into the (hierarchical) mixture of experts model to form a support vector mixture. We show that, in both classification and regression problems, the use of a support vector mixture leads to quadratic programming (QP) problems that are very similar to those for a SVM, with no increase in the dimensionality of the QP pr...

Laplacian twin support vector machine (LapTSVM) is a state-of-the-art nonparallel-planes semi-supervised classifier. It tries to exploit the geometrical information embedded in unlabeled data to boost its generalization ability. However, Lap-TSVM may endure heavy burden in training procedure since it needs to solve two quadratic programming problems (QPPs) with the matrix ‘‘inversion’’ operatio...