Support vector machines are powerful kernel methods for classification and regression tasks. If trained optimally, they produce excellent separating hyperplanes. The quality of the training, however, depends not only on the given training data but also on additional learning parameters, which are difficult to adjust, in particular for unbalanced datasets. Traditionally, grid search techniques have been used for determining suitable values for these parameters. In this paper we propose an automated approach to adjusting the learning parameters using a derivative-free numerical optimizer. To make the optimization process more efficient, a new sensitive quality measure is introduced. Numerical tests with a well-known dataset show that our approach can produce support vector machines that are very well tuned to their classification tasks.

In this paper we analyze support vector machine classification using the soft margin approach that allows for errors and margin violations during the training stage. Two models for learning the separating hyperplane do exist. We study the behavior of the resulting optimization algorithms in terms of training time and test accuracy for unbalanced data sets. The main goal of our work is to compare the features of the resulting classification functions, which are mainly defined by the support vectors arising during the support vector machine training.

We consider the problem of tuning parameters for the learning method support vector machine. The APPSPACK software, an asynchronous parameter tuning method, is well suited for SVM parameter fitting due to several characteristics. No derivative information is needed for bound constrained optimization and the code can be run in parallel mode. Recently, a hybrid parallel support vector machine has been proposed. To couple both parallel packages, the APPSPACK software needs to be customized to allow for a parallel function evaluation in addition to the parallelism provided by APPSPACK. In this paper we describe our customization of the APPSPACK software to facilitate a top down parallelism in SVM parameter tuning.

The increasing amount of data used for classification, as well as the demand for complex models with a large number of well tuned parameters, naturally lead to the search for efficient approaches making use of massively parallel systems. We describe the parallelization of support vector machine learning for shared memory systems. The support vector machine is a powerful and reliable data mining method. Our learning algorithm relies on a decomposition scheme, which in turn uses a special variable projection method, for solving the quadratic program associated with support vector machine learning. By using hybrid parallel programming, our parallelization approach can be combined with the parallelism of a distributed cross validation routine and parallel parameter optimization methods.

The support vector machine (SVM) is a well-established and accurate supervised learning method for the classification of data in various application fields. The statistical learning task -- the so-called training -- can be formulated as a quadratic optimization problem. During the last years the decomposition algorithm for solving this optimization problem became the most frequently used method for support vector machine learning and is the basis of many SVM implementations today. It is characterized by an internal parameter called working set size. Usually small working sets have been assigned. The increasing amount of data used for classification led to new parallel implementations of the decomposition method with efficient inner solvers. With these solvers larger working sets can be assigned. It was shown, that for parallel training with the decomposition algorithm large working sets achieve good speedup values. However, the choice of the optimal working set size for parallel training is not clear. In this paper, we show how the working set size influences the number of decomposition steps, the number of kernel function evaluations and the overall training time in serial and parallel computation.

---

In this paper we describe a new hybrid distributed/shared memory parallel software for support vector machine learning on large data sets. The support vector machine (SVM) method is a well-known and reliable machine learning technique for classification and regression tasks. Based on a recently developed shared memory decomposition algorithm for support vector machine classifier design we increased the level of parallelism by implementing a cross validation routine based on message passing. With this extention we obtained a flexible parallel SVM software that can be used on high-end machines with SMP architectures to process the large data sets that arise more and more in bioinformatics and other fields of research.

Modern classification methods are able to analyze large, complex and sometimes also unbalanced data sets. It is important not only to produce good results but also to control the time to achieve them. High computational costs lead to the exclusion of data mining methods even in case of good accuracy. Our paper deals with support vector machines in view of the consumption of CPU time. We study their learning behavior for unbalanced data sets with increasing size. We also examine the question whether it is necessary and practicable to parallelize this method.

We consider the problem of selecting and tuning learning parameters for support vector machines, especially for the classification of large and unbalanced data sets. We show why and how simple models with few parameters should be refined and propose an automated approach for tuning the increased number of parameters in the extended model. Based on a sensitive quality measure we analyze correlations between the number of parameters, the learning time and the performance of the trained SVM in classifying independent test data. In addition we study the influence of the quality measure on the classification performance and compare the behavior of serial and asynchronous parallel parameter tuning on an IBM p690 cluster.