Rafael Giusti

TL;DR: The longer the time needed for the search, the higher the speedup ratio achieved by the method, and it is demonstrated that the method performs similarly to UCR suite for small queries and narrow warping constraints, but performs up to five times faster for long queries and large warping windows.

TL;DR: This paper empirically compares 48 measures on 42 time series data sets and shows that Complex Invariant Distance DTW (CIDDTW) significantly outperforms DTW and that CIDDTw, DTW, CID, Minkowski L-p (p-norm difference with data set-crafted "p" parameter), Lorentzian L-infinity, Manhattan L-1, Average L-2, Dice distance, and Jaccard distance outperform ED.

TL;DR: Asfault is a low-cost system to evaluate and monitor road pavement conditions in real-time using smartphone sensors and machine learning algorithms and achieves a classification performance superior to 90% in a 5-class problem considering the following road qualities: Good, Average, Fair, and Poor.

TL;DR: This work presents a comprehensive overview of approaches that tackle concept drift in classification problems in an unsupervised manner and includes a proposed taxonomy of state‐of‐the‐art approaches for concept drift detection based on unsuper supervised strategies.

TL;DR: Line Spectral Frequencies (LSF) provides a set of highly predictive coefficients for digit recognition and it is shown that the choice of the right attribute extraction method is more important than the specific classification paradigm, and that the right combination of classifier and attributes can provide almost perfect accuracy.