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  Browse All Reviews > Computing Methodologies (I) > Pattern Recognition (I.5) > Design Methodology (I.5.2) > Feature Evaluation And Selection (I.5.2...)  
  1-10 of 100 Reviews about "Feature Evaluation And Selection (I.5.2...)": Date Reviewed
   Tied factors analysis for high-dimensional image feature extraction and recognition application
Liao H., Chen Y., Dai W., Ruan R.  Pattern Analysis & Applications 20(2): 587-600, 2017. Type: Article

Face recognition is one of those unstructured tasks that the human brain excels at, but that is difficult for computers to perform. But as it becomes crucial for many modern activities, it would be a great help for us humans if we could automate t...

Jun 15 2017
  Context-based unsupervised ensemble learning and feature ranking
Soltanmohammadi E., Naraghi-Pour M., van der Schaar M.  Machine Learning 105(3): 459-485, 2016. Type: Article

An unsupervised ensemble learning and feature ranking method in which the combiner has no information about the expert’s performance, methods, and the data with which they operate is proposed in this paper. The method uses batch processing a...

Apr 20 2017
  Variational Bayesian inference for infinite generalized inverted Dirichlet mixtures with feature selection and its application to clustering
Bdiri T., Bouguila N., Ziou D.  Applied Intelligence 44(3): 507-525, 2016. Type: Article

Model-based data analysis is a powerful, increasingly popular tool for inferring knowledge and abstract information from data sets. To date, the model-based learning literature has mostly been dominated by Gaussian mixtures. However, conventional ...

Jun 20 2016
  A comprehensive performance evaluation of 3D local feature descriptors
Guo Y., Bennamoun M., Sohel F., Lu M., Wan J., Kwok N.  International Journal of Computer Vision 116(1): 66-89, 2016. Type: Article

For those that are working with 3D point clouds (for example, 3D keypoint detection, 3D local feature descriptors, 3D object recognition/classification/retrieval, 3D scene modeling and reconstruction, and 3D data registration, among many other pos...

Apr 27 2016
  Offline scripting-free author identification based on speeded-up robust features
Sharma M., Dhaka V.  International Journal on Document Analysis and Recognition 18(4): 303-316, 2015. Type: Article

Text-independent writer identification (writer ID) has been studied for many years, and more and more techniques are now proposed to deal with various scripts and writing conditions. In general, there are two types of feature extraction: histogram...

Feb 19 2016
  Face and palmprint multimodal biometric systems using Gabor-Wigner transform as feature extraction
Saini N., Sinha A.  Pattern Analysis & Applications 18(4): 921-932, 2015. Type: Article

Biometric identification of humans has many compelling advantages, but some challenges must be overcome. Each biometric modality (face, fingerprint, iris, and so on) has some number of individuals for which it is difficult to apply or inaccurate i...

Feb 9 2016
   Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval
Verma M., Raman B.  Journal of Visual Communication and Image Representation 32(C): 224-236, 2015. Type: Article

Content-based image retrieval (CBIR) is vital for organizing and querying large image sets in useful ways. The general approach is to provide an image, or an image description, and find images that “look like this one.” Challenges incl...

Dec 15 2015
  Multi-modal biological driver monitoring via ubiquitous wearable body sensor network
Dehzangi O., Williams C.  DH 2015 (Proceedings of the 5th International Conference on Digital Health 2015, Florence, Italy,  May 18-20, 2015) 65-70, 2015. Type: Proceedings

Self-driving cars, or driverless cars, are becoming the future of driving without manual intervention. Currently, the speed of around 25 kilometers per hour (km/h) has been achieved for self-driving cars; however, the research in this direction is...

Aug 20 2015
  Machine learning for first-order theorem proving
Bridge J., Holden S., Paulson L.  Journal of Automated Reasoning 53(2): 141-172, 2014. Type: Article, Reviews: (2 of 2)

Bridge and colleagues have developed a methodology of machine learning for theorem proving that uses real-valued features of the problem at hand and determines in a rigorous manner and with fewer preconceptions the possible connections between fea...

Jul 22 2015
   Machine learning for first-order theorem proving
Bridge J., Holden S., Paulson L.  Journal of Automated Reasoning 53(2): 141-172, 2014. Type: Article, Reviews: (1 of 2)

Automatic theorem provers, which today are no longer new things, can be said to be powerful tools in, for example, high-assurance design and verification of systems. Humans certainly need theorem provers’ help for many types of today’s...

Jun 29 2015
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