A gentle introduction to the use of knowledge, logic and inference in machine learning is given. It can be regarded as a reinterpretation and revisiting of Ryzard Michalskiâ s â A theory and methodology of inductive learningâ within the framework of logical and relational learning. At the same time some contemporary issues surrounding the integration of logical and probabilistic representations and reasoning are introduced.Book subtitle: DEDICATED TO THE MEMORY OF PROFESSOR RYSZARD S. MICHALSKIedition: 1ststatus: publishe
53 pages ; Kay R. Amel is the pen name of the working group “Apprentissage et Raisonnement” of the G...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Logic has been a—disputed—ingredient in the emergence and development of the now very large field kn...
Abstract. A gentle introduction to the use of knowledge, logic and in-ference in machine learning is...
I use the term logical and relational learning (LRL) to refer to the subfield of machine learning an...
Probabilistic inductive logic programming (PILP), sometimes also called statistical relational learn...
While the popularity of statistical, probabilistic and exhaustive machine learning techniques still ...
Abstract. Statistical relational learning (SRL) addresses one of the central open questions of AI: t...
This paper aims to be a friendly introduction to formal learning theory. I introduce key concepts at...
Probabilistic inductive logic programming aka. statistical relational learning addresses one of the ...
In the past few years there has been a lot of work lying at the intersection of probability theory, ...
Abstract. Probabilistic inductive logic programming, sometimes also called statistical relational le...
What is the relationship between learning and reasoning? Much recent work in machine learning has be...
Abstract. This is a book with two maín items: One is logic, i.e. deduction, and the other is learnin...
This thesis describes a novel approach to machine learning, based on the principle of learning by re...
53 pages ; Kay R. Amel is the pen name of the working group “Apprentissage et Raisonnement” of the G...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Logic has been a—disputed—ingredient in the emergence and development of the now very large field kn...
Abstract. A gentle introduction to the use of knowledge, logic and in-ference in machine learning is...
I use the term logical and relational learning (LRL) to refer to the subfield of machine learning an...
Probabilistic inductive logic programming (PILP), sometimes also called statistical relational learn...
While the popularity of statistical, probabilistic and exhaustive machine learning techniques still ...
Abstract. Statistical relational learning (SRL) addresses one of the central open questions of AI: t...
This paper aims to be a friendly introduction to formal learning theory. I introduce key concepts at...
Probabilistic inductive logic programming aka. statistical relational learning addresses one of the ...
In the past few years there has been a lot of work lying at the intersection of probability theory, ...
Abstract. Probabilistic inductive logic programming, sometimes also called statistical relational le...
What is the relationship between learning and reasoning? Much recent work in machine learning has be...
Abstract. This is a book with two maín items: One is logic, i.e. deduction, and the other is learnin...
This thesis describes a novel approach to machine learning, based on the principle of learning by re...
53 pages ; Kay R. Amel is the pen name of the working group “Apprentissage et Raisonnement” of the G...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Logic has been a—disputed—ingredient in the emergence and development of the now very large field kn...