Multiple-Instance Learning for Natural Scene Classi cation.
Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data.
In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances.
Multiple instance learning (MIL) is an ideal tool to build a robust classifier on multiview 2D US scans of the same kidneys by treating multi-views of 2D US scans of the same kidney as multiple.
We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria: risk reduction, uncertainty, diversity, density and relevance. We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning.
Multiple-instance learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from which features are.
Our main contribution is a multiple-instance learning-based approach for weakly supervised. 1. category learning from images. Our learning paradigm exploits the wealth of text surrounding natural images that already exists, while properlyaccountingfortheir anticipatednoise and ambigu-ity. Experimental results indicate the approach’s promise.
Explicit Document Modeling through Weighted Multiple-Instance Learning of-the-art neural baselines. The bene t is observed across several datasets, feature spaces, and types of input vectors or intermediate hidden states of a network. 3.We evaluate the explanatory power of our model by comparing its predicted aspect.