Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...
Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously-learned classifier to work under new cond...
Chris Bourke, Kun Deng, Stephen D. Scott, Robert E...
Standard pattern discovery techniques, such as association rules, suffer an extreme risk of finding very large numbers of spurious patterns for many knowledge discovery tasks. The...
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although...
Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendri...
Abstract. We consider a large volume principle for transductive learning that prioritizes the transductive equivalence classes according to the volume they occupy in hypothesis spa...
Although memory-based classifiers offer robust classification performance, their widespread usage on embedded devices is hindered due to the device's limited memory resources...
Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
The importance of providing a mechanism to call C functions from high-level languages has been understood for many years and, these days, almost all statically-typed high-level-la...