We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likeli...
David M. J. Tax, Piotr Juszczak, Robert P. W. Duin...
Abstract--Tao et al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be mor...
In this paper, we propose the first metal-density driven placement algorithm to reduce CMP variation and achieve higher routability. Based on an analytical placement framework, we...
Tung-Chieh Chen, Minsik Cho, David Z. Pan, Yao-Wen...
We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric den...
Tolga Tasdizen, Suyash P. Awate, Ross T. Whitaker,...
In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function tha...