Dissimilarity-Based Multiple Instance Learning

Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the...

Sequence-based learning algorithms for understanding and improving protein characteristics

The development of high-throughput measurement techniques resulted in rapidlyincreasing amounts of biological data, which made computational methodsessential for biological research. Hence, the field of bioinformatics emergedthat since plays an important role in storing, making accessible, integrating,and analysing different types of biological...