This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifi...
Huma Lodhi, Grigoris J. Karakoulas, John Shawe-Tay...
We suggest a formal model to represent and solve the multicast routing problem in multicast networks. To attain this, we model the network adapting it to a weighted and-or graph, ...
In context-dependent acoustic modeling, it is important to strike a balance between detailed modeling and data sufficiency for robust estimation of model parameters. In the past,...
This paper presents a Bayesian method for temporally aligning a music score and an audio rendition. A critical problem in audio-toscore alignment is in dealing with the wide varie...
Akira Maezawa, Hiroshi G. Okuno, Tetsuya Ogata, Ma...
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...