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Explainable AI: A Review of Machine Learning Interpretability Methods
Entropy, 2020 Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks.
Pantelis Linardatos +2 more
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Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
Complexity, 2021 K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents
Stanislav Protasov, Adil Mehmood Khan
doaj +1 more source
Multimodal Machine Learning: A Survey and Taxonomy [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it ...
T. Baltrušaitis +2 more
semanticscholar +1 more source
Journal of Physics A: Mathematical and Theoretical, 2020 Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark ...
Owen Howell +3 more
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Machine learning for nanoplasmonics
Nature Nanotechnology, 2023 Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales.
Masson, Jean-Francois +2 more
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Distributional Prototypical Methods for Reliable Explanation Space Construction
IEEE Access, 2023 As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo +3 more
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Machine Learned Learning Machines
, 2017 There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary
Sheneman, Leigh, Hintze, Arend
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Introduction to Machine Learning [PDF]
, 2013 The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely
Baştanlar, Yalın, Özuysal, Mustafa
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Power Allocation Schemes Based on Deep Learning for Distributed Antenna Systems
IEEE Access, 2020 In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS).
Gongbin Qian +4 more
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Thumbs up? Sentiment Classification using Machine Learning Techniques [PDF]
Conference on Empirical Methods in Natural Language Processing, 2002 We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform ...
B. Pang +2 more
semanticscholar +1 more source