Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization and recommendation, etc. A major challenge lies ...
ErHeng Zhong, Wei Fan, Junwei Wang, Lei Xiao, Yong...
We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by s...
Machine learning methods that can use additional knowledge in their inference process are central to the development of integrative bioinformatics. Inclusion of background knowled...
Minca Mramor, Marko Toplak, Gregor Leban, Tomaz Cu...
Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such ca...
Zeno Gantner, Lucas Drumond, Christoph Freudenthal...
Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should al...
Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-stru...
Two parameters, C and r, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a gene...
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
We report on three distinct experiments that provide new valuable insights into learning algorithms and datasets. We first describe two effective meta-features that significantly ...
Predictive accuracy and parameter plausibility are two major desired aspects for a student modeling approach. Knowledge tracing, the most commonly used approach, suffers from local...