Gabriela Guimarães

Learn More
This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders. The main idea lies in introducing several abstraction levels that allow a step-wise identification of temporal patterns. Self-organizing neural networks(More)
In application domains such as medicine, where a large amount of data is gathered, a medical diagnosis and a better understanding of the underlying generating process is an aim. Recordings of temporal data often afford an interpretation of the underlying pattens. This means that for diagnosis purposes a symbolic, i.e. understandable and interpretable(More)
This paper presents a taxonomy for Self-organizing Maps (SOMs) for temporal sequence processing. Four main application areas for SOMs with temporal processing have been identified. These are prediction, control, monitoring and data mining. Three main techniques have been used to model temporal relations in SOMs: 1) pre-processing or post-processing the(More)
This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical levels enables the discovery of complex structures as present in(More)