25 citations · 41 across the 7 of their papers we have counts for
7 papers
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Michiel Stock, Tapio Pahikkala, Antti Airola +2
Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or…
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Michiel Stock, Krzysztof Dembczynski, Bernard De Baets +1
Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predict…
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method
Michiel Stock, Tapio Pahikkala, Antti Airola +2
Pairwise learning or dyadic prediction concerns the prediction of properties for pairs of objects. It can be seen as an umbrella covering various machine learning problems such as…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Tapio Pahikkala, Antti Airola, Michiel Stock +2
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditione…
A kernel-based framework for learning graded relations from data
Willem Waegeman, Tapio Pahikkala, Antti Airola +3
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations betwe…