Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Linearization Machine Learning Algorithms   This paper is about a machine learning approach based on the multilinear
projection of an unknown function (or probability distribution) to be estimated
towards a linear (or multilinear) dimensional space E'. The proposal transforms
the problem of predicting the target of an observation x into a problem of
determining a consensus among the k nearest neighbors of x's image within the
dimensional space E'. The algorithms that concretize it allow both regression
and binary classification. Implementations carried out using Scala/Spark and
assessed on a dozen LIBSVM datasets have demonstrated improvements in
prediction accuracies in comparison with other prediction algorithms
implemented within Spark MLLib such as multilayer perceptrons, logistic
regression classifiers and random forests.

31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin