transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. feature selection for transfer learning (f-mmd) is a simple and effective transfer learning method, which tackles the domain shift problem. f-mmd has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive perf...

Within the field of process mining, several different trace clustering approaches exist for partitioning traces or instances into similar groups. Typically, this is based on certain patterns similarity between traces, driven by discovery a model each cluster. The main drawback these techniques, however, that their solutions are usually hard to evaluate justify domain experts. In paper, we prese...

Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking. Following the tracking-by-detection framework, an online MILBoost approach is developed that sequentially chooses weak classifiers by maximizing the bag likelihood. In this paper, we extend this idea towards incorporating the instance significance estimation into the online ...

Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints – must-link and cannot-link constraints – and show how they can be incorporated into a clustering algorithm to aid that search. For three of the four data sets tested, our results indicate that the incorporation of surpris...

In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into clusters. The goal is to recover the class labels or to find the subcl...

Various criteria and algorithms can be used for clustering, leading to very distinct outcomes potential biases towards datasets with certain structures. More generally, the selection of most effective algorithm applied a given dataset, based on its characteristics, is problem that has been largely studied in field meta-learning. Recent advances form new methodology known as Instance Space Analy...