Fssd-a fast and efficient algorithm for subgroup set discovery
Subgroup discovery (SD) is the task of discovering interpretable patterns in the data that
stand out wrt some property of interest. Discovering patterns that accurately discriminate a …
stand out wrt some property of interest. Discovering patterns that accurately discriminate a …
[HTML][HTML] Synergies between machine learning and reasoning-An introduction by the Kay R. Amel group
This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
[PDF][PDF] From shallow to deep interactions between knowledge representation, reasoning and machine learning
KR Amel - … Conference Scala Uncertainity Mgmt (SUM 2019) …, 2019 - sum2019.hds.utc.fr
This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
From shallow to deep interactions between knowledge representation, reasoning and machine learning (Kay R. Amel group)
This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
Modeling pattern set mining using boolean circuits
Researchers in machine learning and data mining are increasingly getting used to modeling
machine learning and data mining problems as parameter learning problems over network …
machine learning and data mining problems as parameter learning problems over network …
Targeted and contextual redescription set exploration
M Mihelčić, T Šmuc - Machine learning, 2018 - Springer
One important problem occurring in redescription mining is a very large number of produced
redescriptions. This makes analyses time consuming and generally difficult. We present the …
redescriptions. This makes analyses time consuming and generally difficult. We present the …
Equitable conceptual clustering using OWA operator
We propose an equitable conceptual clustering approach based on multi-agent
optimization, where each cluster is represented by an agent having its own satisfaction. The …
optimization, where each cluster is represented by an agent having its own satisfaction. The …
Exploring and mining attributed sequences of interactions
We consider entities interacting over time: individuals meeting, customers buying products,
etc., each entity being labeled with some information that may depend on time, and possibly …
etc., each entity being labeled with some information that may depend on time, and possibly …