Results 11 to 20 of about 21,772 (300)
Sensors, 2022 In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU)
Molika Meas +7 more
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Sequential Monte Carlo-guided ensemble tracking. [PDF]
PLoS ONE, 2017 A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier.
Yuru Wang +4 more
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Consensual based classification as emergent decisions in a complex system
Jordanian Journal of Computers and Information Technology, 2022 In massive multi-agents systems, that are used to model some complex systems, emergence is a key feature that allows to model high level states of such systems.
Rabah Mazouzi +3 more
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Vietnam Journal of Computer Science, 2023 In intelligent information systems data play a critical role. The issue of missing data is one of the commonplace problems occurring in data collected in the real world. The problem stems directly from the very nature of data collection.
Mateusz Szczepański +3 more
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Multi-View Hand-Hygiene Recognition for Food Safety
Journal of Imaging, 2020 A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses
Chengzhang Zhong +3 more
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Evolutionary design of nearest prototype classifiers [PDF]
, 2004 In pattern classification problems, many works have been carried out with the aim of designing good classifiers from different perspectives. These works achieve very good results in many domains.
Isasi, Pedro +2 more
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Set analysis of coincident errors and its applications for combining classifiers [PDF]
, 2003 Although addressed in many papers, classifier dependency is still not well defined. Continuously being described by variety of statistical models from conditional probability to diversity measures, dependency among classifier out-puts was recently shown ...
Ruta, Dymitr +3 more
core +1 more source
Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts
Applied Computational Intelligence and Soft Computing, 2021 For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift ...
Tinofirei Museba +3 more
openaire +2 more sources
Learning Multi-Tree Classification Models with Ant Colony Optimization [PDF]
, 2014 Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers).
Otero, Fernando E.B. +3 more
core +1 more source
Computers, 2023 The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers.
Lukáš Korel +4 more
doaj +1 more source