5 papers · 1 filter
K-means for Evolving Data Streams
Arkaitz Bidaurrazaga, Aritz Pérez, Marco Capó
Currently the amount of data produced worldwide is increasing beyond measure, thus a high volume of unsupervised data must be processed continuously. One of the main unsupervised d…
Minimax Classification with 0-1 Loss and Performance Guarantees
Santiago Mazuelas, Andrea Zanoni, Aritz Perez
Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-samp…
Rank aggregation for non-stationary data streams
Ekhine Irurozki, Jesus Lobo, Aritz Perez +1
We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the…
General Supervision via Probabilistic Transformations
Santiago Mazuelas, Aritz Perez
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle gener…
An efficient K-means algorithm for Massive Data
Marco Capó, Aritz Pérez, José Antonio Lozano
Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to ma- nipulate and analyze such information. Eve…