Titlebook: Boosted Statistical Relational Learners; From Benchmarks to D Sriraam Natarajan,Kristian Kersting,Jude Shavlik Book 2014 The Author(s) 2014

腫塊

Nausea

Introduction: Where Is Nordic Noir?,ter, we discuss how this algorithm can be adapted to learn to act in sequential domains. We then present three of our most successful applications in real health care data—two cardiovascular prediction problems and the third is prediction of onset of Alzheimer’s disease. We then conclude the chapter with a few NLP applications.

forestry

Boosting (Bi-)Directed Relational Models,es, instead of just one, results in an expressive model for the conditional distributions of RDNs. We then present a sample set of results that show superior performance when compared to state-of-the-art approaches.

LIMN

Boosting Undirected Relational Models,rning undirected SRL models. More precisely, we adapt the algorithm for learning the popular formalism of Markov Logic Networks. We derive the gradients in this case and present empirical evidence to demonstrate the efficacy of this approach.

陰謀

Boosting in the Presence of Missing Data,umed to be false. In this chapter, we relax this assumption and derive a boosting algorithm that can effectively work with missing data. The derivation is independent of the model and hence we will discuss about adapting it for RDNs and MLNs. As with other chapters, we will conclude with empirical evaluation on the SRL data sets.

Inveterate

Boosting Statistical Relational Learning in Action,ter, we discuss how this algorithm can be adapted to learn to act in sequential domains. We then present three of our most successful applications in real health care data—two cardiovascular prediction problems and the third is prediction of onset of Alzheimer’s disease. We then conclude the chapter with a few NLP applications.

NEXUS

cardiac-arrest

DAUNT

摘要