Results 41 to 50 of about 279,520 (289)
Predicting protein function by machine learning on amino acid sequences – a critical evaluation [PDF]
, 2007 Copyright @ 2007 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use ...
Al-Shahib, A, Breitling, R, Gilbert, D
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Application of ensemble learning techniques to model the atmospheric concentration of SO2 [PDF]
Global Journal of Environmental Science and Management, 2019 In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide.
A. Masih
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Quantum ensembles of quantum classifiers
, 2017 Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis.
Petruccione, Francesco, Schuld, Maria
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Learning sentiment from students’ feedback for real-time interventions in classrooms [PDF]
, 2014 Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose,
Altrabsheh, Nabeela +2 more
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Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
, 2018 In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the ...
G Tandon +4 more
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Machine learning classifiers: Evaluation of the performance in online reviews [PDF]
, 2016 This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value.
Pak, Irina *, Teh, Phoey Lee *
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Using Machine Learning to Classify Test Outcomes [PDF]
2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 2019 When testing software it has been shown that there are substantial benefits to be gained from approaches which exercise unusual or unexplored interactions with a system - techniques such as random testing, fuzzing, and exploratory testing. However, such approaches have a drawback in that the outputs of the tests need to be manually checked for ...
openaire +2 more sources
Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification
Complexity, 2018 The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system.
Zeynep H. Kilimci, Selim Akyokus
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Papers in Historical Phonology, 2018 Machine learning is a powerful method when working with large data sets such as diachronic corpora. However, as opposed to standard techniques from inferential statistics like regression modeling, machine learning is less commonly used among phonological
Andreas Baumann
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BMC Cancer, 2022 Background The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients ...
Jia Wang +4 more
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