Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LiteMORT: A memory efficient gradient boosting tree system on adaptive
  compact distributions   Gradient boosted decision trees (GBDT) is the leading algorithm for many
commercial and academic data applications. We give a deep analysis of this
algorithm, especially the histogram technique, which is a basis for the
regulized distribution with compact support. We present three new
modifications. 1) Share memory technique to reduce memory usage. In many cases,
it only need the data source itself and no extra memory. 2) Implicit merging
for "merge overflow problem"."merge overflow" means that merge some small
datasets to huge datasets, which are too huge to be solved. By implicit
merging, we just need the original small datasets to train the GBDT model. 3)
Adaptive resize algorithm of histogram bins to improve accuracy. Experiments on
two large Kaggle competitions verified our methods. They use much less memory
than LightGBM and have higher accuracy. We have implemented these algorithms in
an open-source package LiteMORT. The source codes are available at
https://github.com/closest-git/LiteMORT

21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph