Learn More
In this paper, we summarize systems submitted by PSTL to the evaluation. We ran Meta-Data (MD) on Switchboard (SWB) and Broadcast News (BN) data. Speech-to-text systems were built and tested on both SWB and BN systems with limited real-time constraints. For our first participation, our systems were characterized by low complexity, exploratory operating(More)
We consider the problem of online learning in a changing environment under sparse user feedback. Specifically, we address the classification of music types according to a user's preferences for a hearing aid application. The classifier, operating under limited computational resources, must be capable of adjusting to types of data not represented in the(More)
The sequential data flux in many time-series applications require that only a small fraction of the data are stored for future processing. Furthermore, labels for these data are possibly sparse and they might show some biases. To support learning under such restrictive constraints, we combine manifold regularization with sequential learning under a(More)
Personalization for real-world machine-learning applications usually has to incorporate user feedback. Unfortunately, user feedback often suffers from sparsity and possible inconsistencies. Here we present an algorithm that exploits feedback for learning only when it is consistent. The user provides feedback on a small subset of the data. Based on the data(More)
We present a regularized approach for online learning of a pseudometric in the form of a Mahalanobis distance. We express the problem as an optimization that learns on the current labeled instance whilst favoring a solution of a predefined form. Our focus is on regularization. Our formulation takes up a flexible form allowing for scenarios ranging from(More)
  • 1