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» Using Goal-Models to Analyze Variability
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ESANN
2001
13 years 10 months ago
Input data reduction for the prediction of financial time series
Prediction of financial time series using artificial neural networks has been the subject of many publications, even if the predictability of financial series remains a subject of ...
Amaury Lendasse, John Aldo Lee, Eric de Bodt, Vinc...
INTERSPEECH
2010
13 years 3 months ago
Modeling pronunciation variation with context-dependent articulatory feature decision trees
We consider the problem of predicting the surface pronunciations of a word in conversational speech, using a model of pronunciation variation based on articulatory features. We bu...
Sam Bowman, Karen Livescu
TSE
2008
97views more  TSE 2008»
13 years 8 months ago
Timed Automata Patterns
Timed Automata have proven to be useful for specification and verification of real-time systems. System design using Timed Automata relies on explicit manipulation of clock variabl...
Jin Song Dong, Ping Hao, Shengchao Qin, Jun Sun 00...
SIGMETRICS
1998
ACM
14 years 12 days ago
Self-Similarity in File Systems
We demonstrate that high-level le system events exhibit selfsimilar behaviour, but only for short-term time scales of approximately under a day. We do so through the analysis of f...
Steven D. Gribble, Gurmeet Singh Manku, Drew S. Ro...
ICDM
2008
IEEE
224views Data Mining» more  ICDM 2008»
14 years 3 months ago
A Non-parametric Approach to Pair-Wise Dynamic Topic Correlation Detection
We introduce dynamic correlated topic models (DCTM) for analyzing discrete data over time. This model is inspired by the hierarchical Gaussian process latent variable models (GP-L...
Yang Song, Lu Zhang 0007, C. Lee Giles