Recent local state-of-the-art stereo algorithms based on variable cost aggregation strategies allow for inferring disparity maps comparable to those yielded by algorithms based on ...
Stefano Mattoccia, Simone Giardino, Andrea Gambini
Inspired by our past manual aspect mining experiences, this paper describes a random walk model to approximate how crosscutting concerns can be discovered in the absence of domain...
Understanding the structure of multidimensional patterns, especially in unsupervised case, is of fundamental importance in data mining, pattern recognition and machine learning. Se...
We propose an efficient algorithm for mining frequent approximate sequential patterns under the Hamming distance model. Our algorithm gains its efficiency by adopting a "brea...
Anomaly detection is an important data mining task. Most existing methods treat anomalies as inconsistencies and spend the majority amount of time on modeling normal instances. A r...