Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of
  Efficiency within the Confines of the Time Series Classification Task   Time series classification is one of the very popular machine learning tasks.
In this paper, we explore the application of Hidden Markov Model (HMM) for time
series classification. We distinguish between two modes of HMM application. The
first, in which a single model is built for each class. The second, in which
one HMM is built for each time series. We then transfer both approaches for
classifier construction to the domain of Fuzzy Cognitive Maps. The identified
four models, HMM NN (HMM, one per series), HMM 1C (HMM, one per class), FCM NN,
and FCM 1C are then studied in a series of experiments. We compare the
performance of different models and investigate the impact of their
hyperparameters on the time series classification accuracy. The empirical
evaluation shows a clear advantage of the one-model-per-series approach. The
results show that the choice between HMM and FCM should be dataset-dependent.

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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
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The categories of this paper are: eess
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
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The categories of this paper are: solv-int
29
The categories of this paper are: funct-an