Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
We believe that intelligent information agents will represent their users interest in electronic marketplaces and other forums to trade, exchange, share, identify, and locate goods...
In an open environment such as the Internet, the decision to collaborate with a stranger (e.g., by granting access to a resource) is often based on the characteristics (rather tha...
In this investigation, we propose a probabilistic approach for estimating the ages of Blog authors by means of Naive Bayesian Classifier. We can learn context of characteristic wor...