Neural Computation
Approximation of the Determinant of Large Sparse Symmetric Positive Definite Matrices
SIAM Journal on Matrix Analysis and Applications
Model Selection and Error Estimation
Machine Learning
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Proceedings of the 25th international conference on Machine learning
The loss rank principle for model selection
COLT'07 Proceedings of the 20th annual conference on Learning theory
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
| Hi-index | 0.03 |
A key issue in statistics and machine learning is to automatically select the ''right'' model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle-the Loss Rank Principle (LoRP)-for model selection in regression and classification. It is based on the loss rank, which counts how many other (fictitious) data would be fitted better. LoRP selects the model that has minimal loss rank. Unlike most penalized maximum likelihood variants (AIC, BIC, MDL), LoRP depends only on the regression functions and the loss function. It works without a stochastic noise model, and is directly applicable to any non-parametric regressor, like kNN.