Ellipsoidal Multiple Instance Learning

Multiple Instance Learning via Covariant Aggregation

We present a multiple instance learning (MIL) algorithm that learns ellipsoidal decision boundaries with arbitrary covariance. In contrast to the fixed-length feature vectors of traditional classification problems, MIL operates on unordered bags of instances. Commonly, each instance is a feature vector, and a bag is considered positive if any one of its instances is positive. In training data, ...

Different Learning Levels in Multiple-choice and Essay Tests: Immediate and Delayed Retention

    This study investigated the effects of different learning levels, including Remember an Instance (RI), Remember a Generality (RG), and Use a Generality (UG) in multiple-choice and essay tests on immediate and delayed retention. Three-hundred pre-intermediate students participated in the study. Reading passages with multiple-choice and essay questions in different levels of learning were giv...

Revisiting multiple instance neural networks

Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In...

Instance-level Semisupervised Multiple Instance Learning

Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-based image retrieval and text categorization can be viewed as MIL problems. In this paper, we propose a new graph-based semi-supervised learning approach for multiple instance learning. By defining an instance-level g...

Integrating Deep Learning and Multiple Instance Learning