Parameter and Structure Learning Algorithms for Statistical Relational Learning
نویسندگان
چکیده
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 the two. This is the focus of the field of Statistical Relational Learning. My research is based on the use of the vast plethora of techniques developed in the field of Logic Programming, in which the distribution semantics [16] is one of the most prominent approaches. This semantics underlies, e.g., Probabilistic Logic Programs,Probabilistic Horn Abduction,PRISM [16], Independent Choice Logic,pD,Logic Programs with Annotated Disjunctions (LPADs) [17], ProbLog [5] and CP-logic. These languages have the same expressive power: there are linear transformations from one to the others. LPADs offer the most general syntax, so my research and experimentations has been focused on this formalism. An LPAD consists of a finite set of disjunctive clauses, where each of the disjuncts in the head of a clause is annotated with the probability of the disjunct to hold, if the body of the clause holds. LPADs are particularly suitable when reasoning about actions and effects where we have causal independence among the possible different outcomes for a given action. Various works have appeared for solving three types of problems for languages under the distribution semantics:
منابع مشابه
An Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملTractable Learning of Liftable Markov Logic Networks
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Markov networks with first-order logic. Unfortunately, inference and maximum-likelihood learning with MLNs is highly intractable. For inference, this problem is addressed by lifted algorithms, which speed up inference by exploiting symmetries. State-of-the-art lifted algorithms give tractability gu...
متن کاملAdaptive Incremental Learning for Statistical Relational Models Using Gradient-Based Boosting
We consider the problem of incrementally learning models from relational data. Most existing learning methods for statistical relational models use batch learning, which becomes computationally expensive and eventually infeasible for large datasets. The majority of the previous work in relational incremental learning assumes the model’s structure is given and only the model’s parameters needed ...
متن کاملStatistical Learning for Relational and Structured Data
Learning with relational and structured data has gained a growing interest within the machine learning community in recent years. The great development of this research area has been mainly due to the large amount of available data, organized in complex relational structures, coming from a variety of fields, like molecular biology, social networks analysis, natural language parsing, and many ot...
متن کاملClass-Level Bayes Nets for Relational Data
Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning has developed a number of new statistical models for such data. Most of these models aim to support instance-level predictions about the attributes or links of specific entities. In this paper we focus on learning class-l...
متن کاملContext-based statistical relational learning
The relational structure is an important source of information, which is often ignored by the traditional statistical learning methods. Thus this thesis focuses on how to explicitly exploit such relational information in statistical learning tasks so as to build more effective and more robust models. The main methodology used in the thesis is derived from context-based modeling and analysis. Se...
متن کامل