Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory
versatility and accuracy in fields such as drug discovery because they are based on …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

Recently, discriminative training (DT) methods have achieved tremendous progress in
automatic speech recognition (ASR). In this survey article, all mainstream DT methods in …

Supervised topic models utilize document's side information for discovering predictive low
dimensional representations of documents; and existing models apply likelihood-based …

2022-07-20 Assigned to CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS
COLLATERAL AGENT reassignment CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH …

Machine Learning: Discriminative and Generative covers the main contemporary themes
and tools in machine learning ranging from Bayesian probabilistic models to discriminative …

Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially
conceived priors to incorporate domain knowledge for discovering improved latent …

In this article, we studied the objective functions of MMI, MCE, and MPE/MWE for
discriminative learning in sequential pattern recognition. We presented an approach that …

Extreme electricity prices occur with a higher frequency and a larger magnitude in recent
years. Accurate forecasting of the occurrence of extreme prices is of great concern to market …

Most of the Bayesian network-based classifiers are usually only able to handle discrete
variables. However, most real-world domains involve continuous variables. A common …