246 citations · 367 across the 6 of their papers we have counts for
6 papers
The Feature Importance Ranking Measure
Alexander Zien, Nicole Kraemer, Soeren Sonnenburg +1
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access…
Regularized estimation of large-scale gene association networks using graphical Gaussian models
Nicole Kraemer, Juliane Schaefer, Anne-Laure Boulesteix
Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exce…
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
Nicole Kraemer, Masashi Sugiyama, Mikio Braun
The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees o…
Kernel Partial Least Squares is Universally Consistent
Gilles Blanchard, Nicole Kraemer
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Leas…
Sparse Causal Discovery in Multivariate Time Series
Stefan Haufe, Guido Nolte, Klaus-Robert Mueller +1
Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belongin…
Penalized Partial Least Squares Based on B-Splines Transformations
Nicole Kraemer, Anne-Laure Boulesteix, Gerhard Tutz
We propose a novel method to model nonlinear regression problems by adapting the principle of penalization to Partial Least Squares (PLS). Starting with a generalized additive mode…