Add to ORKGProbability Estimation Trees (PETs) try to estimate the probability with which an instance belongs to a certain class, rather than just predicting its most likely class. Several approaches for learning PETs have been proposed, mainly in a propositional context. Since we are interested in applying PETs in a relational context, we make some simple modifications to the first-order tree learner Tilde to incorporate the main approaches (and a novel variant) and we experiment with all of them. Our results provide insight into the strengths and weaknesses of the alternative approaches in a relational context.Technical Report TUM-I0510, Technische Universität Münchenstatus: publishe
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
Classification learning is a type of supervised machine learning technique that uses a classificatio...
Many approaches to probabilistic logical learning have been proposed by now, and several of these ha...
Probability trees (or Probability Estimation Trees, PET's) are decision trees with probability distr...
Probability trees (or Probability Estimation Trees, PET's) are decision trees with probability...
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic cl...
Recently, there has been an increasing interest in probabilistic logical models and a variety of suc...
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic cl...
Probability trees (or Probability Estimation Trees, PET's) are decision trees with probability distr...
Probabilistic inductive logic programming (PILP), sometimes also called statistical relational learn...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Abstract First-order model counting recently emerged as a computational tool for high-level probabil...
Over the past two decades, statistical machine learning approaches to natural language processing ha...
A recursive probability tree (RPT) is an incipient data structure for representing the distributions...
Statistical relational models combine aspects of first-order logic and probabilistic graphical model...
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
Classification learning is a type of supervised machine learning technique that uses a classificatio...
Many approaches to probabilistic logical learning have been proposed by now, and several of these ha...
Probability trees (or Probability Estimation Trees, PET's) are decision trees with probability distr...
Probability trees (or Probability Estimation Trees, PET's) are decision trees with probability...
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic cl...
Recently, there has been an increasing interest in probabilistic logical models and a variety of suc...
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic cl...
Probability trees (or Probability Estimation Trees, PET's) are decision trees with probability distr...
Probabilistic inductive logic programming (PILP), sometimes also called statistical relational learn...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Abstract First-order model counting recently emerged as a computational tool for high-level probabil...
Over the past two decades, statistical machine learning approaches to natural language processing ha...
A recursive probability tree (RPT) is an incipient data structure for representing the distributions...
Statistical relational models combine aspects of first-order logic and probabilistic graphical model...
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
Classification learning is a type of supervised machine learning technique that uses a classificatio...
Many approaches to probabilistic logical learning have been proposed by now, and several of these ha...