In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training...
In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization p...
Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
...
Rong Jin (Michigan State University), Shijun Wang...
This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach,...
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...