Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Transformations for Classification Forests   This work introduces a transformation-based learner model for classification
forests. The weak learner at each split node plays a crucial role in a
classification tree. We propose to optimize the splitting objective by learning
a linear transformation on subspaces using nuclear norm as the optimization
criteria. The learned linear transformation restores a low-rank structure for
data from the same class, and, at the same time, maximizes the separation
between different classes, thereby improving the performance of the split
function. Theoretical and experimental results support the proposed framework.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th