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Parameter and Structure Learning Algorithms for Statistical Relational Learning [PDF]
, 2012 My research activity focuses on the field of Machine Learning. Two key challenges in most machine learning applications are uncertainty and complexity. The standard framework for handling uncertainty is probability, for complexity is first-order logic. Thus we would like to be able to learn and perform inference in representation languages that combine
BELLODI, Elena, RIGUZZI, Fabrizio
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
Latent feature networks for statistical relational learning
2018In this dissertation, I explored relational learning via latent variable models. Traditional machine learning algorithms cannot handle many learning problems where there is a need for modeling both relations and noise. Statistical relational learning approaches emerged to handle these applications by incorporating both relations and uncertainties in ...
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Latent factor models for statistical relational learning
2014To simplify modeling procedures, traditional statistical machine learning methods always assume that the instances are independent and identically distributed (i.i.d.). However, it is not uncommon for some real-world data, such as web pages and research papers, to contain relationships (links) between the instances. Different instances in such data are
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Logic-based Formalisms for Statistical Relational Learning
2007This chapter provides a selective overview of logic-based approaches to statistical relational learning. Issues of representation, inference, and learning are addressed with an emphasis on representation. A distinction is drawn between “directed” representations with connections to Bayesian nets and “undirected” ones related to Markov nets.
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Statistical relational learning with nonparametric Bayesian models
2007Statistical relational learning analyzes the probabilistic constraints between the entities, their attributes and relationships. It represents an area of growing interest in modern data mining. Many leading researches are proposed with promising results. However, there is no easily applicable recipe of how to turn a relational domain (e.g.
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Statistical learning from relational data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003openaire +1 more source