We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artiicial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art ...

We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: 1) an approximate cross-validation scheme to estimate the smoothing parameter inherent in regression splines, as well as 2) Monte Carlo expectation maximization (MCEM) and 3) Bayesian methods to fit the regression spline model. We compare the approximate cross-validation app...

5 × 2 cross-validated F-test based on independent five replications of 2-fold cross-validation is recommended in choosing between two classification learning algorithms. However, the reusing of the same data in a 5 × 2 cross-validation causes the real degree of freedom (DOF) of the test to be lower than the F(10, 5) distribution given by (Neural Comput 11:1885–1892, [1]). This easily leads the ...

The feasibility of determining the relative populations of multi-conformer structures from NOE-derived distances alone is assessed. Without cross-validation of the NOE restraints, any population ratio can be refined to a similar quality of the fit. Complete cross-validation provides a less biased measure of fit and allows the estimation of the correct population ratio when used in conjunction w...

In the paper we consider new estimators of expected values E w(X) of functions of a random variable X. The new estimators are based on Gauss quadrature, a numerical method frequently used to approximate integrals over finite intervals. We apply the new estimators in Partial Cross Validation, a numerical method for finding optimal smoothing parameters in nonparametric curve estimation. We show t...

In the sequential approach to three-dimensional reconstruction, adding prior knowledge about camera pose improves reconstruction accuracy. We add a smoothing penalty on the camera trajectory. The smoothing parameter, usually fixed by trial and error, is automatically estimated using Cross-Validation. This technique is extremely expensive in its basic form. We derive Gauss-Newton Cross-Validatio...

A key problem in time series prediction using autoregressive models is to fix the model order, namely the number of past samples required to model the time series adequately. The estimation of the model order using cross-validation is a long process. In this paper we explore faster alternative to cross-validation, based on nonlinear dynamics methods, namely Grassberger-Procaccia, Kégl and False...

The paper presents results from simulations based on real data, comparing several competing mean squared error of prediction (MSEP) estimators on principal components regression (PCR) and partial least squares regression (PLSR): leave-one-out crossvalidation, K-fold and adjusted K-fold cross-validation, the ordinary bootstrap estimate, the bootstrap smoothed cross-validation (BCV) estimate and ...

Mika et al. [1] apply the “kernel trick” to obtain a non-linear variant of Fisher’s linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark datasets. We show that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O(l3) operations rather than the O(l4) of a näıve impl...

Tuning parameters in supervised learning problems are often estimated by cross-validation. The minimum value of the cross-validation error can be biased downward as an estimate of the test error at that same value of the tuning parameter. We propose a simple method for the estimation of this bias that uses information from the cross-validation process. As a result, it requires essentially no ad...