We formulate an online learning algorithm that exploits the temporal smoothness of data evolving on trajectories in a temporal manifold. The learning agent builds an undirected graph whose nodes store the information provided by the data during the input evolution. The agent’s behavior is based on a dynamical system that is derived from the temporal coherence assumption for the prediction function. Moreover, the graph connections are developed in order to implement a regularization process in both the spatial and temporal dimensions. The algorithm is evaluated on a benchmark based on a temporal sequence obtained from the MNIST dataset by generating a video from the original images. The proposed approach is compared with standard methods when the number of supervisions decreases.
Scheda prodotto non validato
Scheda prodotto in fase di analisi da parte dello staff di validazione
|Titolo:||On-line Learning on Temporal Manifolds|
|Citazione:||Maggini, M., & Rossi, A. (2016). On-line Learning on Temporal Manifolds. In AI*IA 2016 Advances in Artificial Intelligence: XVth International Conference of the Italian Association for Artificial Intelligence (pp.321-333). Srpinger International Publishers.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|
File in questo prodotto: