Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model Selection with the Loss Rank Principle   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.

19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th