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Moderating k-NN Classifiers
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf01238023openalex-snapshot
Combining classifiers: A theoretical framework
inferredcites →https://doi.org/10.1016/0893-6080(94)90099-xopenalex-snapshot
Combining the results of several neural network classifiers
inferredcites →https://doi.org/10.1016/s0167-8655(99)00107-5openalex-snapshot
Experimental evaluation of expert fusion strategies
inferredcites →https://doi.org/10.1023/a:1007607513941openalex-snapshot
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
inferredcites →https://doi.org/10.1049/ip-vis:19970987openalex-snapshot
Generalised approach to the recognition of structurally similar handwritten characters using multiple expert classifiers
inferredcites →https://doi.org/10.1109/21.155943openalex-snapshot
Methods of combining multiple classifiers and their applications to handwriting recognition
inferredcites →https://doi.org/10.1109/34.273716openalex-snapshot
Decision combination in multiple classifier systems
inferredcites →https://doi.org/10.1109/34.368145openalex-snapshot
A method of combining multiple experts for the recognition of unconstrained handwritten numerals
inferredcites →https://doi.org/10.1109/34.588027openalex-snapshot
Combination of multiple classifiers using local accuracy estimates
inferredcites →https://doi.org/10.1109/icpr.2000.906040openalex-snapshot
Improving the performance of the product fusion strategy
inferredcites →https://doi.org/10.1117/12.421124openalex-snapshot
Population bias control for bagging k-NN experts
inferredcites →https://doi.org/10.1162/neco.1994.6.2.181openalex-snapshot
Hierarchical Mixtures of Experts and the EM Algorithm
inferredcites →https://doi.org/10.48780/publications.aston.ac.uk.00010603openalex-snapshot
High level data fusion
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Airway Tree Extraction with Locally Optimal Paths
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Image Dissimilarity-Based Quantification of Lung Disease from CT
inferred← citeshttps://doi.org/10.1007/978-3-642-22092-0_39openalex-snapshot
Dissimilarity-Based Classification of Anatomical Tree Structures
inferred← citeshttps://doi.org/10.1007/s10044-007-0084-8openalex-snapshot
Recognising facial expressions in video sequences
inferred← citeshttps://doi.org/10.1007/s10044-011-0242-xopenalex-snapshot
Classification using distances from samples to linear manifolds
inferred← citeshttps://doi.org/10.1007/s11390-005-0005-5openalex-snapshot
Adapt Bagging to Nearest Neighbor Classifiers
inferred← citeshttps://doi.org/10.1016/j.eswa.2009.02.065openalex-snapshot
Boosting k-nearest neighbor classifier by means of input space projection
inferred← citeshttps://doi.org/10.1016/j.nicl.2016.11.025openalex-snapshot
Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry
inferred← citeshttps://doi.org/10.1109/icpr.2004.1334630openalex-snapshot
Image segmentation by shape particle filtering
inferred← citeshttps://doi.org/10.1109/tmi.2006.872747openalex-snapshot
Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification
inferred← citeshttps://doi.org/10.1109/tmi.2009.2013851openalex-snapshot
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
inferred← citeshttps://doi.org/10.1109/tmi.2011.2164931openalex-snapshot
Texture-Based Analysis of COPD: A Data-Driven Approach
inferred← citeshttps://doi.org/10.1109/tmi.2011.2168420openalex-snapshot
Automated Brain Structure Segmentation Based on Atlas Registration and Appearance Models
inferred← citeshttps://doi.org/10.1109/tsmcb.2005.845396openalex-snapshot
Ensembling Local Learners Through Multimodal Perturbation
inferred← citeshttps://doi.org/10.1117/12.2006038openalex-snapshot
Local appearance features for robust MRI brain structure segmentation across scanning protocols
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Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial
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A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification
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