Learn More
The structure of a Markov network is typically learned in one of two ways. The first approach is to treat this task as a global search problem. However, these algorithms are slow as they require running the expensive operation of weight (i.e., parameter) learning many times. The second approach involves learning a set of local models and then combining them(More)
Network medicine is an emerging paradigm for studying the co-occurrence between diseases. While diseases are often in-terlinked through complex patterns, most of the existing work in this area has focused on studying pairwise relationships between diseases. In this paper, we use a state-of-the-art Markov network learning method to learn interactions between(More)
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Markov networks with first-order logic. Unfortunately , inference and maximum-likelihood learning with MLNs is highly intractable. For inference , this problem is addressed by lifted algorithms , which speed up inference by exploiting symmetries.(More)
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combines Markov networks with first-order logic. MLNs attach weights to formulas in first-order logic. Learning MLNs from data is a challenging task as it requires searching through the huge space of possible theories. Additionally, evaluating a theory’s likelihood(More)
Association football is becoming increasingly competitive and the financial stakes involved are causing football clubs and football leagues to become more professional. Over the past 25 years, club budgets have grown enormously due to ticket sale revenues, broadcasting revenues, merchandising, and prize money. Recently , player tracking systems were(More)
This paper proposes a relational-learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial configurations of the players on the court) and temporal (that is, the order of events and(More)
Considerable amounts of data are continuously generated by pathologists in the form of pathology reports. To date, there has been relatively little work exploring how to apply machine learning and data mining techniques to these data in order to extract novel clinical relationships. From a learning perspective, these pathology data possess a number of(More)
Sports analytics had its public breakthrough as early as the 1970s when baseball enthusiasts started developing a range of statistical tools for analyzing players, teams, and strategies. Due to a combination of early successes, increased computational power and advanced, automated data collection methods, sports analytics has been a steadily growing area in(More)