Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Confusion Matrix Stability Bounds for Multiclass Classification   In this paper, we provide new theoretical results on the generalization
properties of learning algorithms for multiclass classification problems. The
originality of our work is that we propose to use the confusion matrix of a
classifier as a measure of its quality; our contribution is in the line of work
which attempts to set up and study the statistical properties of new evaluation
measures such as, e.g. ROC curves. In the confusion-based learning framework we
propose, we claim that a targetted objective is to minimize the size of the
confusion matrix C, measured through its operator norm ||C||. We derive
generalization bounds on the (size of the) confusion matrix in an extended
framework of uniform stability, adapted to the case of matrix valued loss.
Pivotal to our study is a very recent matrix concentration inequality that
generalizes McDiarmid's inequality. As an illustration of the relevance of our
theoretical results, we show how two SVM learning procedures can be proved to
be confusion-friendly. To the best of our knowledge, the present paper is the
first that focuses on the confusion matrix from a theoretical point of view.

18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph