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Sonia Castelo, Moacir Ponti and Rosane Minghim
Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming a...
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Andrei Konstantinov, Lev Utkin and Vladimir Muliukha
A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed. A new type of soft decision trees is considered, which is similar to the w...
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Shikha Dubey, Abhijeet Boragule, Jeonghwan Gwak and Moongu Jeon
Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection. We propose a framework, Deep-network with Multiple Ranking ...
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Annabella Astorino, Antonio Fuduli, Giovanni Giallombardo and Giovanna Miglionico
A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, wh...
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Napsu Karmitsa and Sona Taheri
Nonsmooth optimization refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. This Special Issue contains six research articles that collect together the most recent techniques and applications in the are...
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