Traversing the Class Boundary: Gone Girl (2014) as Failed Remake

2019 ◽  
Vol 6 (1-2) ◽  
pp. 215-221
Author(s):  
Anthony Ballas
Keyword(s):  
Class Boundary

Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition

2020 ◽  
Vol 58 (8) ◽  
pp. 5782-5792
Author(s):  
Sihang Dang ◽  
Zongjie Cao ◽  
Zongyong Cui ◽  
Yiming Pi ◽  
Nengyuan Liu
Keyword(s):  
Incremental Learning ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Class Boundary

A class boundary preserving algorithm for data condensation

2011 ◽  
Vol 44 (3) ◽  
pp. 704-715 ◽  
Author(s):  
K. Nikolaidis ◽  
J.Y. Goulermas ◽  
Q.H. Wu
Keyword(s):  
Class Boundary

Existence of outgoing solutions for perturbations of and applications to the scattering matrix

1992 ◽  
Vol 111 (2) ◽  
pp. 399-415
Author(s):  
Kazuhiro Yamamoto
Keyword(s):  
Boundary Condition ◽  
Exterior Domain ◽  
Dirichlet Boundary ◽  
Scattering Matrix ◽  
Normal Vector ◽  
Elliptic Differential Operator ◽  
Potential Term ◽  
Class Boundary ◽  
Image Position ◽  
Outer Normal Vector

In this paper we shall prove an existence theorem and give applications of an outgoing solution of the following problem:where L(x, x) is a second order elliptic differential operator with a potential term q(x), is an exterior domain of ℝn (where n 2) with the C2-class boundary , k is an element of the complex plane or of a logarithmic Riemann surface, and B is either a Dirichlet boundary condition or of the form Bu = vj(x) ajk(x) ku + (x)u with the unit outer normal vector v(x) = (vl,, vn) at x.

An Improved KNN Classification Method Based on Variable Precision Rough Set

2014 ◽  
Vol 513-517 ◽  
pp. 978-982
Author(s):  
Xun Wang ◽  
Li Sha Liu ◽  
Qing Hu Wang ◽  
Jian Hong Qi ◽  
Ming Yang Jiang ◽  
...  
Keyword(s):  
Rough Set ◽  
High Efficiency ◽  
Training Sample ◽  
Classification Method ◽  
Variable Precision ◽  
Knn Classification ◽  
Class Boundary ◽  
Variable Precision Rough Set ◽  
Sample Set ◽  
Classification Efficiency

KNN classifier is a simple, non-parametric, high efficiency algorithm. But it has the defect that the classification efficiency will increase as the enlargement of data scale. This paper put forward a new KNN classification method based on rough set on the research of algorithms presented by Yu Ying and Heping Gou. The innovation of this algorithm is that the kernel, negative domain and boundary region of variable precision rough set are used to be the indexes of measuring the intra-class, extra-class and the class boundary of training sample set. The samples of intra-class, extra-class and the class boundary that are to be classed have differential treatment when judging the category. In this way, the scale of training sample set is effectively reduced and the efficiency and precision of classification are improved. At last, the category function is improved to reflect the category of samples to be classed. The experimental results show that the method can obviously improve the classification efficiency and accuracy.

The Low/High BCS Permeability Class Boundary: Physicochemical Comparison of Metoprolol and Labetalol

2014 ◽  
Vol 11 (5) ◽  
pp. 1707-1714 ◽  
Author(s):  
Moran Zur ◽  
Marisa Gasparini ◽  
Omri Wolk ◽  
Gordon L. Amidon ◽  
Arik Dahan
Keyword(s):  
Class Boundary
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