In many problems from different disciplines such as engineering, physics, medicine, and biology, a series of experimental data is used in order to generate a model that can describe a system with minimum noise. The procedure for building a model provides a description of the behavior of the system under study and can be used to give a prediction for the future. Herein a novel hierarchical bi-le...

The idea of cross-validation is simple and easy to implement: split the data into several blocks, leave out one block for model estimation, and predict the values of the left-out block. Those predictions are then used to compute a certain prediction loss function. Even though the basic procedure is simple, some additional programming effort is necessary for more complex procedures such as repea...

The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006), and introduces the R package CVThresh implementing details of the calculations for the procedures. This procedure is implemented by coupling a conventional c...

The restoration problem deals with images in which information has been destroyed or obscured. In this paper, we present a framework for addressing image restoration problems in which the goal is to recover information about the image. Restoration algorithms often use tentative assumptions to compensate for the information lost in the degradation process. We propose cross-validation as a method...

We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artiicial data and theoretical results in restricted settings have shown that for selecting a good classiier from a set of classi-ers (model selection), tenfold cross-validation may be better than the more expensive leave-one-out cross-validation. We repo...

We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report o...

Model selection is one of the key issues both in recent research and application of kernel methods. Cross-validation is a commonly employed and widely accepted model selection criterion. However, it requires multiple times of training the algorithm under consideration, which is computationally intensive. In this paper, we present a novel strategy for approximating the cross-validation based on ...