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TSP
2010
13 years 2 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
CSL
1999
Springer
13 years 7 months ago
A hidden Markov-model-based trainable speech synthesizer
This paper presents a new approach to speech synthesis in which a set of cross-word decision-tree state-clustered context-dependent hidden Markov models are used to define a set o...
R. E. Donovan, Philip C. Woodland
PR
2010
147views more  PR 2010»
13 years 5 months ago
Minimum classification error learning for sequential data in the wavelet domain
Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted ...
D. Tomassi, Diego H. Milone, L. Forzani
CORR
2010
Springer
69views Education» more  CORR 2010»
13 years 7 months ago
Epistemic irrelevance in credal nets: the case of imprecise Markov trees
We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal ne...
Gert de Cooman, Filip Hermans, Alessandro Antonucc...
ICIP
2002
IEEE
14 years 9 months ago
Unsupervised image segmentation via Markov trees and complex wavelets
The goal in image segmentation is to label pixels in an image based on the properties of each pixel and its surrounding region. Recently Content-Based Image Retrieval (CBIR) has e...
Cián W. Shaffrey, Ian Jermyn, Nick G. Kings...