Since both the least mean-square (LMS) and least mean-fourth (LMF) algorithms suffer individually from the problem of eigenvalue spread, so will the mixed-norm LMS-LMF algorithm. Therefore, to overcome this problem for the mixed-norm LMS-LMF, we are adopting here the same technique of normalization (normalizing with the power of the input) that was successfully used with the LMS and LMF separat...

In this paper, we present the Maximum Normalized Likelihood Estimation (MNLE) algorithm and its application for discriminative training of HMMs for continuous speech recognition. The objective of this algorithm is to maximize the normalized frame likelihood of training data. Instead of gradient descent techniques usually applied for objective function optimization in other discriminative algori...

Abstract: The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is proposed. The step size of the MEE algorithm is normalized with the power of input entropy...

The Normalized Recurrence Algorithm is a kind of localist attractor network describing the temporal dynamics in continuous and recurrent information integration emerging in experimental psychology data (The Continuity of Mind, Michael Spivey, 2007). Despite the fact that this algorithm successfully models time series data, it is somewhat unsatisfactory to deal with an algorithm within a dynamic...

A Zahid ME(SP)

A normalized gaussian network (NGnet) (Moody & Darken, 1989) is a network of local linear regression units. The model softly partitions the input space by normalized gaussian functions, and each local unit linearly approximates the output within the partition. In this article, we propose a new on-line EMalgorithm for the NGnet, which is derived from the batch EMalgorithm (Xu, Jordan, &Hinton 19...

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the a...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used ...

Nearly Orthogonal Two-Dimensional Grid Generation with Aspect Ratio Control Volkan Akcelik,∗ Branislav Jaramaz,†,‡ and Omar Ghattas∗ ∗Laboratory for Mechanics, Algorithms, and Computing, Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania; †Center for Orthopaedic Research, UPMC Shadyside Hospital, Pittsburgh, Pennsylvania; and ‡Robotics Instit...