background: numerous studies used microarray gene expression data to extract metastasis-driving gene signatures for the prediction of breast cancer relapse. however, the accuracy and generality of the previously introduced biomarkers are not acceptable for reliable usage in independent datasets. this inadequacy is attributed to ignoring gene interactions by simple feature selection methods, due...

As we know, M-estimation as objective function can be used to tackle this problem that the performance of wavelet network (WN) is affected by gross error severely, but its influence function is determined by the absolute value of residual, so a key problem is how to choose initial parameters. In this paper combining robust estimation with wavelet support vector machine (WSVM), a robust wavelet ...

This paper deals with two subjects. First, we will show how support vector machine (SVM) regression problem can be solved as the maximum a posteriori prediction in the Bayesian framework. The second part describes an approximation technique that is useful in performing calculations for SVMs based on the mean ÿeld algorithm which was originally proposed in Statistical Physics of disordered syste...

Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we ...

A novel classification method based on regression is proposed in this paper and then the equivalences of the classification and regression are demonstrated by using numerical experiments under the framework of support vector machine. The proposed algorithm implements the classification tasks by the way used in regression problems. It is more efficiently for multi-classification problems since i...

Due to the increasingly demand for spatio-temporal analysis, time series and spatial statistics are extended to the spatial dimension and the temporal dimension respectively or they are combined via linear regression. However, such linear regression is just a simplification of complicated spatio-temporal associations existing in complex geographical phenomena. In this study, the Support Vector ...

This article explained the financial innovation service product fundamental mode of supply chain finance, and explored the risk of supply chain finance. Fuzzy ordinal regression support vector machine is used to analysis the risk of supply chain finance by the index system of risk assessment, and the results were effective and could be improved in the future.

This article demonstrates a new conceptor network based classifier in classifying images. Mathematical descriptions and analysis are presented. Various tests are experimented in this context using three benchmark datasets: MNIST, CIFAR10 and CIFAR-100. The experiments displayed that conceptor network can offer superior results and flexible configurations than conventional classifiers such as So...

• §2 motivates and outlines binary support vector machines. The contents of this section are standard, and the reader is referred to [Vapnik, 1995] for more details. However, we will follow a non-standard presentation; instead of motivating support vector machines from the point of view of optimal separating hyper-planes, we focus on the relationship between logistic regression and support vect...