We describe a novel framework for class noise mitigation that assigns a vector of class membership probabilities to each training instance, and uses the confidence on the current ...
Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic contro...
Corruption of data by class-label noise is an important practical concern impacting many classification problems. Studies of data cleaning techniques often assume a uniform label ...
High noise robustness has been achieved in speech recognition by using sparse exemplar-based methods with spectrogram windows spanning up to 300 ms. A downside is that a large exe...
Antti Hurmalainen, Jort F. Gemmeke, Tuomas Virtane...
Abstract. We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries,...