Add to ORKGthis paper we will describe a relational instance-based algorithm which we terme
This article describes a learning classifier system (LCS) approach to relational reinforcement learn...
International audienceKnowledge graphs and other forms of relational data have become awidespread ki...
This dissertation introduces a framework for specifying instance-based algorithms that can solve sup...
Abstract. Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. H...
Instance based learning and clustering are popular methods in propositional machine learning. Both m...
Abstract. In this paper we present a novel and general framework for kernel-based learning over rela...
This paper presents an introduction to reinforcement learning and relational reinforcement learning ...
This thesis is specialized in instance based learning algorithms. Main goal is to create an applicat...
Abstract. Instance Based Methods for classification are based on storing the complete training datas...
The presented thesis focuses on instance-based learning (IBL) methods. The groundwork of instance-ba...
ii Instance-based learning is a machine learning method that classifies new examples by comparing th...
Relational reinforcement learning (RRL) is a Q-learning technique which uses first or-der regression...
Instance-based learning is a machine learning method that classifies new examples by comparing them ...
Abstract. Attribute-value based representations, standard in today’s data mining systems, have a lim...
This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users int...
This article describes a learning classifier system (LCS) approach to relational reinforcement learn...
International audienceKnowledge graphs and other forms of relational data have become awidespread ki...
This dissertation introduces a framework for specifying instance-based algorithms that can solve sup...
Abstract. Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. H...
Instance based learning and clustering are popular methods in propositional machine learning. Both m...
Abstract. In this paper we present a novel and general framework for kernel-based learning over rela...
This paper presents an introduction to reinforcement learning and relational reinforcement learning ...
This thesis is specialized in instance based learning algorithms. Main goal is to create an applicat...
Abstract. Instance Based Methods for classification are based on storing the complete training datas...
The presented thesis focuses on instance-based learning (IBL) methods. The groundwork of instance-ba...
ii Instance-based learning is a machine learning method that classifies new examples by comparing th...
Relational reinforcement learning (RRL) is a Q-learning technique which uses first or-der regression...
Instance-based learning is a machine learning method that classifies new examples by comparing them ...
Abstract. Attribute-value based representations, standard in today’s data mining systems, have a lim...
This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users int...
This article describes a learning classifier system (LCS) approach to relational reinforcement learn...
International audienceKnowledge graphs and other forms of relational data have become awidespread ki...
This dissertation introduces a framework for specifying instance-based algorithms that can solve sup...