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Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
[article]
2018
arXiv
pre-print
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This paper proposes an agreement rate initialized maximum likelihood estimator (ARIMLE) to optimally fuse the base classifiers. ...
Extensive experiments on visually evoked potential classification in a brain-computer interface application show that ARIMLE outperforms majority voting, and also achieves better or comparable performance ...
This paper proposes a new classifier combination approach, agreement rate initialized maximum likelihood estimator (ARIMLE), to aggregate the base classifiers. ...
arXiv:1805.04740v1
fatcat:5ufawh7gsjanxgmzjvo2qyllmi
Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce
2018
IEEE Transactions on Cybernetics
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It is critical to extract knowledge and build modplexes, which calls for the need for mechanisms to detect some els from big data. ...
These classification prediction for large-scale and complex brain data in useless features often diminish the learning process associterms of efficiency and feasibility. ated with classification algorithms ...
ACKNOWLEDGMENT The authors would like to express their sincere appreciation to the anonymous reviewers for their insightful comments, which greatly improved the quality of this paper. ...
doi:10.1109/tcyb.2018.2859342
pmid:30130243
fatcat:lk7dgvlfhjcj3exex3ddlgss7q
Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface
2016
Frontiers in Neuroscience
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Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however ...
In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers ...
Army Research Laboratory administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and USARL. ...
doi:10.3389/fnins.2016.00430
pmid:27713685
pmcid:PMC5032911
fatcat:m4wiwdfp65do3mpgcvohsttnei
Bayesian networks in neuroscience: a survey
2014
Frontiers in Computational Neuroscience
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(C) The dynamic BN unfolded in time for three time slices. ...
the brain aspects to be studied. ...
from EPFL) and the European Union Seventh Framework Programme (FP7/2007(FP7/ -2013 under grant agreement no. 604102 (Human Brain Project). ...
doi:10.3389/fncom.2014.00131
pmid:25360109
pmcid:PMC4199264
fatcat:2ip7hztt4fexdj5cw4a2gpmgbu
A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme
2017
Frontiers in Human Neuroscience
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In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). ...
This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement. ...
., and Bagheri, N. (2013). Mutliple classifier system for EEG signal classification with application to brain computer interfaces. Neural Comput. ...
doi:10.3389/fnhum.2017.00254
pmid:28596725
pmcid:PMC5443161
fatcat:crrghmfacbeylbirzsxh2ostyi
Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates
2019
Network Neuroscience
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We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space. ...
Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical ...
The resultant distribution of composite scores was used to estimate the maximum value of the likelihood function for the model. ...
doi:10.1162/netn_a_00104
pmid:31637340
pmcid:PMC6779267
fatcat:hjfcdn2vszdh3mamuvmfiehauq
Forecasting methods for cloud hosted resources, a comparison
2015
2015 11th International Conference on Network and Service Management (CNSM)
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under-estimation rates), Overload Likelihood Ratio and Overloaded State Likelihood Ratio. ...
This produces a maximum likelihood estimation of the initial distribution over all states. Let us look at the series produced after digitising the example data we have been using thus far. ...
The Yule-Walker equations are used to estimate the Auto-regression, AR(p) of order p, model parameters φ i . ...
doi:10.1109/cnsm.2015.7367335
dblp:conf/cnsm/EngelbrechtG15
fatcat:dqmmritvuzaztmyamj4ex6t33u
Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep
2021
Sensors
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and contributing factors for overall device success. ...
Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations ...
Informed Consent Statement: Not Applicable. Data Availability Statement: Not Applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21155071
fatcat:vzkczyuc6bdzrn5f745ha6odr4
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes
2021
Briefings in Bioinformatics
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We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. ...
Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). ...
It is unrealistic to assume that AI applications in health are ethically neutral. ...
doi:10.1093/bib/bbaa369
pmid:33406530
pmcid:PMC8424395
fatcat:rgsf4pdmbvdcdmcwz4zf3hwaca
Modelling the dynamic pattern of surface area in basketball and its effects on team performance
2018
Journal of Quantitative Analysis in Sports (JQAS)
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analysis of a game with respect to surface areas dynamics and (ii) to investigate its influence on the points made by both the team and the opponent. ...
Then, we perform descriptive analyses in order to highlight associations between regimes and relevant game variables. ...
Theoretically, the characterisation of the nonparametric maximum likelihood estimate is studied and the algorithm is guaranteed to converge to the unique maximum likelihood estimate. ...
doi:10.1515/jqas-2018-0041
fatcat:b3qwsi7tqjg2vdo7gbjtiorv6m
Histopathological Image Analysis: A Review
2009
IEEE Reviews in Biomedical Engineering
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This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe. ...
Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. ...
Learning ensembles or multiple classifier systems are methods for improving classification accuracy through aggregation of several similar classifiers' predictions TABLE III THE AVERAGE ACCURACY RESULTS ...
doi:10.1109/rbme.2009.2034865
pmid:20671804
pmcid:PMC2910932
fatcat:a6sm4iy5gffbhlc23dtlp7xe2q
A Holistic Strategy for Classification of Sleep Stages with EEG
2022
Sensors
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In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification ...
As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. ...
The EEG, apart from sleep analysis, helps in many other important applications, such as motor imagery classification [9] , visual feedback classification [10] , subject independent brain-computer interface ...
doi:10.3390/s22093557
pmid:35591246
pmcid:PMC9103466
fatcat:odonx7ek7vbrjaosjdwef4vetm
The cloudUPDRS app: A medical device for the clinical assessment of Parkinson's Disease
2018
Pervasive and Mobile Computing
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The app follows closely Part III of the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or ...
in the community unsupervised; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. ...
In all cases, acceleration is recorded along three axes in m/s 2 at the maximum supported sampling rate (at least 50 Hz) and timestamped at maximum resolution (typically microseconds). ...
doi:10.1016/j.pmcj.2017.12.005
fatcat:zmoucmxpsjcr5mjvcnidiqpphq
Reconstructing networks
[article]
2021
arXiv
pre-print
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Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory. ...
This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. ...
In this case, computing the joint posterior distribution for µ and ν returns constraints implying that the inferred error rates are bounded by the maximum and minimum inferred connection probabilities ...
arXiv:2012.02677v2
fatcat:2rwmpvmrqnbafcmkannssavyly
Multi-Source Multi-Domain Data Fusion for Cyberattack Detection in Power Systems
2021
IEEE Access
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It is an ensemble based classifier in which a diverse collection of classifiers (decision trees) VOLUME 4, 2021 are constructed by incorporating randomness in tree construction. ...
DATA FUSION SOFTWARE APPLICATION A desktop application as a data fusion framework is developed for data aggregation, feature extraction, transformation, fusion, and learning purposes as illustrated in ...
doi:10.1109/access.2021.3106873
fatcat:4aemwsqnunhvpavu426fzkvhg4
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