Reconhecimento de padrões por processos adaptativos de compressão

• Ano de defesa: 2020
• Autor(a) principal: Bailão, Adriano Soares de Oliveira
• Orientador(a): Soares, Anderson da Silva
• Banca de defesa: Soares, Anderson da Silva, Silva, Nadia Felix Felipe da, Duque, Cláudio Gottschalg, Costa, Ronaldo Martins da, Monaco, Francisco José
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação em Rede UFG/UFMS (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Inteligência computacional; Reconhecimento de padrões; Aprendizado de máquina; Análise de agrupamentos; Compressão de dados
• Palavras-chave em Inglês: Computational intelligence; Pattern recognition; Machine learning; Cluster analysis; Data compression
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/10565

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.