Reconhecimento de padrões por processos adaptativos de compressão
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2020-03-02
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Universidade Federal de Goiás
Resumo
Data compression is a process widely used by the industry in the storage and transport of
information and is applied to a variety of domains such as text, image, audio and video. The
compression processes are a set of mathematical operations that aim to represent each
sample of data in compressed form, or with a smaller size. Pattern recognition techniques can
use compression properties and metrics to design machine learning models from adaptive
algorithms that represent samples in compressed form. An advantage of adaptive
compression models, is that they have dimensionality reduction techniques resulting from the
compression properties. This thesis proposes a general unsupervised learning model (for
different problem domains and different types of data), which combines adaptive compression
strategies in two phases: granulation, responsible for the perception and representation of the
knowledge necessary to solve a problem generalization, and the codification phase,
responsible for structuring the reasoning of the model, based on the representation and
organization of the problem objects. The reasoning expressed by the model denotes the ability
to generalize data objects in the general context. Generic methods, based on compactors
(without loss of information), lack generalization capacity for some types of data objects, and
in this thesis, lossy compression techniques are also used, in order to circumvent the problem
and increase the capacity of generalization of the model. Results demonstrate that the use of
techniques and metrics based on adaptive compression produce a good approximation of the
original data samples in data sources with high dimensionality. Tests point to good machine
learning models with good generalization capabilities derived from the approach based on the
reduction of dimensionality offered by adaptive compression processes.
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Citação
BAILÃO, A. S. O. Reconhecimento de padrões por processos adaptativos de compressão. 2020. 160 f. Tese (Doutorado em Ciência da Computação em Rede) - Universidade Federal de Goiás, Goiânia, 2020.