In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in that the MDP's true reward function is assumed to be unknown. We show how to frame apprenticeship learning as a linear programming problem, and show that using an off-the-shelf LP solver to solve this problem results in a substantial improvement in running time over existing methods -up to two orders of magnitude faster in our experiments. Additionally, our approach produces stationary policies, while all existing methods for apprenticeship learning output policies that are "mixed", i.e. randomized combinations of stationary policies. The technique used is general enough to convert any mixed policy to a stationary policy.
Enzymes play central roles in metabolic pathways, and the prediction of metabolic pathways in newly sequenced genomes usually starts with the assignment of genes to enzymatic reactions. However, genes with similar catalytic activity are not necessarily similar in sequence, and therefore the traditional sequence similarity-based approach often fails to identify the relevant enzymes, thus hindering efforts to map the metabolome of an organism.Here we study the direct relationship between basic protein properties and their function. Our goal is to develop a new tool for functional prediction (e.g., prediction of Enzyme Commission number), which can be used to complement and support other techniques based on sequence or structure information. In order to define this mapping we collected a set of 453 features and properties that characterize proteins and are believed to be related to structural and functional aspects of proteins. We introduce a mixture model of stochastic decision trees to learn the set of potentially complex relationships between features and function. To study these correlations, trees are created and tested on the Pfam classification of proteins, which is based on sequence, and the EC classification, which is based on enzymatic function. The model is very effective in learning highly diverged protein families or families that are not defined on the basis of sequence. The resulting tree structures highlight the properties that are strongly correlated with structural and functional aspects of protein families, and can be used to suggest a concise definition of a protein family.
We use an EM algorithm to learn user models in a spoken dialog system. Our method requires automatically transcribed (with ASR) dialog corpora, plus a model of transcription errors, but does not otherwise need any manual transcription effort. We tested our method on a voice-controlled telephone directory application, and show that our learned models better replicate the true distribution of user actions than those trained by simpler methods and are very similar to user models estimated from manually transcribed dialogs.
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