Results 1  10
of
670,794
Adaptive blind deconvolution of linear channels using Renyi entropy with Parzen windowing estimation
 IEEE Transactions on Signal Processing
, 2004
"... Abstract. Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiplechannel applications. In this paper, we investigate the suitability of a class of Parzenwindowbased entropy estimates, namely Renyi’s entropy, as a criterion for bl ..."
Abstract

Cited by 14 (3 self)
 Add to MetaCart
Abstract. Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiplechannel applications. In this paper, we investigate the suitability of a class of Parzenwindowbased entropy estimates, namely Renyi’s entropy, as a criterion
www.elsevier.com/locate/pr A novel image thresholding method based on Parzen window estimate
, 2007
"... Image segmentation is one of the most important and fundamental tasks in image processing and techniques based on image thresholding are typically simple and computationally efficient. However, the image segmentation results depend heavily on the chosen image thresholding methods. In this paper, his ..."
Abstract
 Add to MetaCart
, histogram is integrated with the Parzen window technique to estimate the spatial probability distribution of graylevel image values, and a novel criterion function is designed. By optimizing the criterion function, an optimal global threshold is obtained. The experimental results for synthetic real
ParzenWindow Network Intrusion Detectors
 In Proceedings of the Sixteenth International Conference on Pattern Recognition
, 2002
"... Network intrusion detection is the problem of detecting anomalous network connections caused by intrusive activities. Many intrusion detection systems proposed before use both normal and intrusion data to build their classifiers. However, intrusion data are usually scarce and difficult to collect. W ..."
Abstract

Cited by 52 (0 self)
 Add to MetaCart
. We propose to solve this problem using a novelty detection approach. In particular, we propose to take a nonparametric density estimation approach based on Parzenwindow estimators with Gaussian kernels to build an intrusion detection system using normal data only. To facilitate comparison, we have
Manifold Parzen Windows
 Advances in Neural Information Processing Systems 15
, 2002
"... The similarity between objects is a fundamental element of many learning algorithms. Most nonparametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly n ..."
Abstract

Cited by 40 (10 self)
 Add to MetaCart
significant improvements with respect to Parzen density estimators. The density estimators can also be used within Bayes classifiers, yielding classification rates similar to SVMs and much superior to the Parzen classifier.
On the Use of Windows for Harmonic Analysis With the Discrete Fourier Transform
 Proc. IEEE
, 1978
"... AhmwThis Pw!r mak = available a concise review of data win compromise consists of applying windows to the sampled daws pad the ^ affect On the Of in the data set, or equivalently, smoothing the spectral samples. '7 of aoise9 m the ptesence of sdroag bar The two operations to which we subject ..."
Abstract

Cited by 645 (0 self)
 Add to MetaCart
, windowing is less related to sampled windows for DFT's. HERE IS MUCH signal processing devoted to detection and estimation. Detection is the task of determiningif a specific signal set is present in an observation, while estimation is the task of obtaining the values of the parameters
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract

Cited by 766 (29 self)
 Add to MetaCart
Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Manifold Parzen Windows
 Advances in Neural Information Processing Systems 15
, 2002
"... The similarity between objects is a fundamental element of many learning algorithms. Most nonparametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly n ..."
Abstract
 Add to MetaCart
significant improvements with respect to Parzen density estimators. The density estimators can also be used within Bayes classifiers, yielding classification rates similar to SVMs and much superior to the Parzen classifier.
Manifold Parzen Windows
"... Abstract The similarity between objects is a fundamental element of many learning algorithms. Most nonparametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, inparticular to exploit the local invariances in the data or to capture the possi ..."
Abstract
 Add to MetaCart
significant improvements with respect to Parzen densityestimators. The density estimators can also be used within Bayes classifiers, yielding classification rates similar to SVMs and much superior tothe Parzen classifier. 1 Introduction In [1], while attempting to better understand and bridge the gap between
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
Abstract

Cited by 2109 (30 self)
 Add to MetaCart
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The core of this method is a simple hillclimbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distancebased method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximumlikelihood programs and much higher than the performance of distancebased and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximumlikelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distancebased and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page:
Kernel Density Estimation Parzen Windows
"... Let’s temporarily assume the region R is a ddimensional hypercube with hn being the length of an edge. The volume of the hypercube is given by Vn = h d n. (11) We can derive an analytic expression for kn: Define a windowing function: ϕ(u) = 1 uj  ≤ 1/2 j = 1,..., d 0 otherwise (12) This windowin ..."
Abstract
 Add to MetaCart
Let’s temporarily assume the region R is a ddimensional hypercube with hn being the length of an edge. The volume of the hypercube is given by Vn = h d n. (11) We can derive an analytic expression for kn: Define a windowing function: ϕ(u) = 1 uj  ≤ 1/2 j = 1,..., d 0 otherwise (12
Results 1  10
of
670,794