A Feedforward Constructive Neural Network Algorithm for Multiclass Tasks Based on Linear SeparabilityConstructive neural network algorithmLS-discriminant learningBarycentric Correction ProcedureMulticlass classificationConstructive neural network (CoNN) algorithms enable the architecture of a neural network to be ...
If two sets are linearly separable (LS), then there exists a single layer perceptron feedforward neural network that classifies them. We propose three methods for testing linear separability. The first method is based on the notion of convex hull, the second on a halting upper bound for the ...
layer hidden unit to form hidden layer space, and hidden layer transforms input vector. The input data transformation of low dimensional space is mapped into high-dimensional space, so that the problem of linear separability in low-dimensional space can be realized in high-dimensional space. ( )...
necessary and sufficient condition can be formulated in terms of linear separability, analogously as the well-known condition of learnability by perceptrons. ... R Legenstein,W Maass - 《Neural Computation》 被引量: 280发表: 2005年 Experiments with linear and nonlinear feature transformations in HMM...
Inseparability of Go and Stop in Inhibitory Control: Go Stimulus Discriminability Affects Stopping Behavior Inhibitory control, the ability to stop or modify preplanned actions under changing task conditions, is an important component of cognitive functions. Two ... N Ma,AJ Yu - 《Frontiers in Neu...
JunQi, ...YunYang, inJournal of Biomedical Informatics, 2018 5.2.4Others LinearDiscriminant Analysis(LDA) is alinear classifierthat enables us to reduce the data dimensions through projecting a dataset onto a lower-dimensional space with goodclass separability[101]. Formula(2)defines the optimal dis...
AdvancedTopicsinLearningandVisionMing-HsuanYangmhyang@csie.ntu.edu.twLecture6(draft)Announcements•Morecoursematerialavailableonthecoursewebpage•Project..
The classical Cover results on linear separability of points in Rd are a milestone in neural network theory. Nevertheless they are not valid for digital input networks because in this case the points are not, in general position, vertices of a d-dimensional hypercube. The author shows here ...
The choice of a particular testing algorithm has effects on the performance of constructive neural network algorithms that are based on the transformation of a nonlinear separability classification problem into a linearly separable one. This paper presents an empirical study of these effects in terms ...
D. A. Elizondo, J. O. de Lazcano-Lobato, R. Birkenhead, "Choice effect of linear separability testing methods on constructive neural network algorithms: An empirical study", Expert Systems with Applications, Vol. 38, pp. 2330-2346, 2011....