Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Easy to Use Repository for Comparing and Improving Machine Learning
  Algorithm Usage   The results from most machine learning experiments are used for a specific
purpose and then discarded. This results in a significant loss of information
and requires rerunning experiments to compare learning algorithms. This also
requires implementation of another algorithm for comparison, that may not
always be correctly implemented. By storing the results from previous
experiments, machine learning algorithms can be compared easily and the
knowledge gained from them can be used to improve their performance. The
purpose of this work is to provide easy access to previous experimental results
for learning and comparison. These stored results are comprehensive -- storing
the prediction for each test instance as well as the learning algorithm,
hyperparameters, and training set that were used. Previous results are
particularly important for meta-learning, which, in a broad sense, is the
process of learning from previous machine learning results such that the
learning process is improved. While other experiment databases do exist, one of
our focuses is on easy access to the data. We provide meta-learning data sets
that are ready to be downloaded for meta-learning experiments. In addition,
queries to the underlying database can be made if specific information is
desired. We also differ from previous experiment databases in that our
databases is designed at the instance level, where an instance is an example in
a data set. We store the predictions of a learning algorithm trained on a
specific training set for each instance in the test set. Data set level
information can then be obtained by aggregating the results from the instances.
The instance level information can be used for many tasks such as determining
the diversity of a classifier or algorithmically determining the optimal subset
of training instances for a learning algorithm.

24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess