activity
20102017
most citedA two-step learning approach for solving full and almost full cold start problems in dyadic prediction

4 citations · 7 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20241 cited

Flexible framework for generating synthetic electrocardiograms and photoplethysmograms

Katri Karhinoja, Antti Vasankari, Jukka-Pekka Sirkiä +3

By generating synthetic biosignals, the quantity and variety of health data can be increased. This is especially useful when training machine learning models by enabling data augme…

stat.AP20172 cited

Playtime Measurement with Survival Analysis

Markus Viljanen, Antti Airola, Jukka Heikkonen +1

Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various du…

cs.LG20144 cited

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…

cs.LG2014

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…

stat.ML20101 cited

Linear Time Feature Selection for Regularized Least-Squares

Tapio Pahikkala, Antti Airola, Tapio Salakoski

We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector…