In this paper, kernel feature selection is proposed to improve generalization performance of boosting classifiers. Kernel feature Selection attains the feature selection and model selection at the same time using a simple selection algorithm. The algorithm automatically selects a subset of kernel features for each classifier and combines them according to the LogitBoost algorithm. The system em...

The present study aims to compare the Kernel equating and local methods in observed score equating. Functions error estimates regarding difference between raw equated scores by Stocking-Lord Haebara true-score were examined Item Response Theory Observed Score Equating. Therefore, 5, 10, 15 external anchor items used, obtained from two forms based on 2PL model. R (version 3.5.3.) programming sof...

small area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  generally, in some cases, small area estimation uses parametric modeling.  but in fact, a lot of models have no linear relationship between the small area average and the covariat...

background isolated small gut mesentery injury after blunt abdominal trauma from the steering wheel in road traffic accidents is rare. these are always challenging to diagnose and pose a diagnostic dilemma. objectives to study the pattern of small gut mesenteric injury by steering wheel blunt abdominal trauma in road traffic accidents in patients who had laparotomy. patients and methods a 10-ye...

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increase...

We propose a supervised and localized dimensionality reduction method that combines multiple feature representations or kernels. Each feature representation or kernel is used where it is suitable through a parametric gating model in a supervised manner for efficient dimensionality reduction and classification, and local projection matrices are learned for each feature representation or kernel. ...

We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: generalized Fisher discriminant analysis (GDA) and kernel principal components analysis (KPCA) in classification problems. We evaluate the classification performance of the ...

In this paper, a new face recognition method based on kernel discriminate local preserve projection(KDLPP) and Multi-feature fusion under smart environment is proposed . In order to solve the small sample size problem, combined with kernel theory and QR decomposition, a new face recognition algorithm named kernel discriminate local preserve projection is proposed based on discriminate local pre...

We propose a new 3D kernel for the recovery of 3D orientation signatures. In the Cartesian coordinates, the kernel has a shape of a truncated cone with its axis in the radial direction and very small angular support. In the local spherical coordinates, the angular part of the kernel is a 2D Gaussian function. A set of such kernels is obtained by uniformly sampling the 2D space of azimuth and el...