Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast classification using sparse decision DAGs   In this paper we propose an algorithm that builds sparse decision DAGs
(directed acyclic graphs) from a list of base classifiers provided by an
external learning method such as AdaBoost. The basic idea is to cast the DAG
design task as a Markov decision process. Each instance can decide to use or to
skip each base classifier, based on the current state of the classifier being
built. The result is a sparse decision DAG where the base classifiers are
selected in a data-dependent way. The method has a single hyperparameter with a
clear semantics of controlling the accuracy/speed trade-off. The algorithm is
competitive with state-of-the-art cascade detectors on three object-detection
benchmarks, and it clearly outperforms them when there is a small number of
base classifiers. Unlike cascades, it is also readily applicable for
multi-class classification. Using the multi-class setup, we show on a benchmark
web page ranking data set that we can significantly improve the decision speed
without harming the performance of the ranker.

33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci