Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Supervised and Unsupervised Learning for GIS Classification   This paper presents a new hybrid learning algorithm for unsupervised
classification tasks. We combined Fuzzy c-means learning algorithm and a
supervised version of Minimerror to develop a hybrid incremental strategy
allowing unsupervised classifications. We applied this new approach to a
real-world database in order to know if the information contained in unlabeled
features of a Geographic Information System (GIS), allows to well classify it.
Finally, we compared our results to a classical supervised classification
obtained by a multilayer perceptron.

35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg