We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that predicts only based on class labels of related neighbors, using no learning and no inherent attributes.We show that it performs surprisingly well by comparing it to more complex models such as Probabilistic Relational Models and Relational Probability Trees on three data sets from published work. We argue that a simple model such as this should be used as a baseline to assess the performance of relational learners.NYU, Stern School of Business, IOMS department, Center for Digital Economy Researc
To simplify modeling procedures, traditional statistical machine learning methods always assume that...
In the field of machine learning, methods for learning from single-table data have received much mor...
AbstractThis paper studies the connections between relational probabilistic models and reference cla...
We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that predicts...
Abstract. We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model tha...
This paper evaluates several modifications of the Simple Bayesian Classifier to enable estimation an...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
Relational learning refers to learning from data that have a complex structure. This structure may ...
The vast majority of work in Machine Learning has focused on propositional data which is assumed to ...
Many domains exhibit natural relational structures—from the world wide web to scientific publication...
Many domains exhibit natural relational structures—from the world wide web to scientific publication...
Many domains exhibit natural relational structures—from the world wide web to scientific publication...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
To simplify modeling procedures, traditional statistical machine learning methods always assume that...
In the field of machine learning, methods for learning from single-table data have received much mor...
AbstractThis paper studies the connections between relational probabilistic models and reference cla...
We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that predicts...
Abstract. We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model tha...
This paper evaluates several modifications of the Simple Bayesian Classifier to enable estimation an...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
Relational learning refers to learning from data that have a complex structure. This structure may ...
The vast majority of work in Machine Learning has focused on propositional data which is assumed to ...
Many domains exhibit natural relational structures—from the world wide web to scientific publication...
Many domains exhibit natural relational structures—from the world wide web to scientific publication...
Many domains exhibit natural relational structures—from the world wide web to scientific publication...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
When entities are linked by explicit relations, classification methods that take advantage of the ne...
To simplify modeling procedures, traditional statistical machine learning methods always assume that...
In the field of machine learning, methods for learning from single-table data have received much mor...
AbstractThis paper studies the connections between relational probabilistic models and reference cla...