Results 221 to 230 of about 3,121,843 (279)

Comparing human text classification performance and explainability with large language and machine learning models using eye-tracking. [PDF]

Sci Rep
Divya Venkatesh J, Jaiswal A, Nanda G.
europepmc   +1 more source

Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines. [PDF]

Sci Data
Valencia EM, Maki KA, Dootz JN, Barb JJ.
europepmc   +1 more source

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes. [PDF]

Sensors (Basel)
Shaw HO, Devin KM, Tang J, Jiang L.
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Classification performance of sEMG and kinematic parameters for distinguishing between non-lame and induced lameness conditions in horses. [PDF]

Front Vet Sci
St George LB, Spoormakers TJP, Hobbs SJ, Clayton HM, Roy SH, Richards J, Serra Bragança FM.   +6 more
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Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance. [PDF]

PeerJ Comput Sci
Thirugnanasambandam K, Murugan J, Ramalingam R, Rashid M, Raghav RS, Kim TH, Sampedro GA, Abisado M.   +7 more
europepmc   +1 more source

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Classification of Visual Performance

Optometry and Vision Science, 1976
To the Editor. —Classification of visual performance has traditionally been according to the simple dichotomy of "legally seeing" vs "legally blind." This oversimplification has ignored the fact that there is another group with unique problems—patients with low vision . They differ from the blind in that they have usable vision.
B. E. Spivey, A. Colenbrander
openaire   +2 more sources

Predicting Sleep Classification Performance without Labels

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020
When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring.
Kaare B, Mikkelsen, Yousef R, Tabar, Preben, Kidmose   +2 more
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Independence, Measurement Complexity, and Classification Performance

IEEE Transactions on Systems, Man, and Cybernetics, 1975
If f(x) and g(x) are the densities for the N-dimensional measurement vector x, conditioned on the classes c1 and c2, and if finite sets of samples from the two classes are available, then a decision function based on estimates f(x) and ?(x) can be used to classify future observations.
Chandrasekaran, B., Jain, Anil K.
openaire   +2 more sources