Mixtures of latent variable models for density estimation and classification
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Nous passons en revue des techniques de rééchantillonnage utilisées pour l'estimation de variance en sondage. Les techniques de rééchantillonnage considérées sont basées sur la linéarisation, le jackknife, les répétitions équilibrées répétées, et le bootst ...
This work deals with factorial models for multiple time series. Its core content puts it at the interface between statistics and finance. After a brief description of the historical link between the two sciences, it reviews the literature on factorial mode ...
In this paper we aim to explore what is the most appropriate number of data samples needed when measuring the temporal correspondence between a chosen set of video and audio cues in a given audio-visual sequence. Presently the optimal model that connects s ...
In this paper we present a method of parsing unstructured textual records briefly describing a person and their direct relatives. The string `Stephanus, brother of Johannes Magnin, from Saillon' is a typical example of a record. We wish to annotate every t ...
For classification problems, it is important that the classifier is trained with data which is likely to appear in the future. Discriminative models, because of their nature to focus on the boundary between classes rather than data itself, usually do not h ...
In the "missing data" (MD) approach to noise robust automatic speech recognition (ASR), speech models are trained on clean data, and during recognition sections of spectral data dominated by noise are detected and treated as "missing". However, this all-or ...
This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is a continuous density hiddden Markov model trained using either the raw data, or data transformed using Principal Component Analysis or Independe ...
Geographic Information Science methods and tools are likely to help to extract useful and so far unknown information from large spatially explicit genetic datasets to understand the distribution of diversity among and within sheep and goat breeds. Consider ...
In the "missing data" (MD) approach to noise robust automatic speech recognition (ASR), speech models are trained on clean data, and during recognition sections of spectral data dominated by noise are detected and treated as "missing". However, this all-or ...
This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is a continuous density hiddden Markov model trained using either the raw data, or data transformed using Principal Component Analysis or Independe ...