Weighted Parzen windows for pattern classification

Babich, G.A.; Camps, Octavia

doi:10.1109/34.494647

Cited by 97 publications

(31 citation statements)

References 10 publications

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“…3 is the size of data set [12][13][14]. While calculating the density function, the windowing results multiply by the density function, but as the objective is to obtain coefficient for data, this multiplication is not required.…”

Section: Density Weighted K-nearest Neighbors Algorithm For Outliers mentioning

confidence: 99%

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Akben¹,

Alkan²

2015

JEE

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Abstract:In KNN (K-Nearest Neighbour) method, the distance-weighted algorithm is applied in order to reduce the effect of noisy data. However, as this weighting algorithm will be insufficient when the outliers in the training set are so close to the test element, a new weighting algorithm is required for KNN. In this study, instead of distance-weighting, a new density-weighted KNN algorithm is proposed for reducing the effect of noisy data. In the first stage of the proposed method, the coefficient of density of each element in the training set was obtained by Parzen window method. And then, the membership of each test element was determined according to the total of density coefficients (weights) of neighbours belonging to the same class. As for the last stage, the performance results of the frequently used KNN methods and the proposed method (Density-weighted KNN, Classical KNN and Distance-weighted KNN) were compared. The obtained results have shown that the proposed method is more successful by almost 1% than classical KNN method, by 9% than distance weighted KNN method. Moreover, it is more successful than other KNN methods when the test element is so close to the training set elements.

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Section: Density Weighted K-nearest Neighbors Algorithm For Outliers mentioning

confidence: 99%

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Akben¹,

Alkan²

2015

JEE

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“…The kernel function used in this paper is called the Parzen Gaussian kernel [19]. It is given by (5) For the ToA estimation problem, the matrix is set to be an identity matrix of size .…”

Section: A Zero Memory Estimationmentioning

confidence: 99%

Dynamic model-based filtering for mobile terminal location estimation

McGuire

Plataniotis

2003

IEEE Trans. Veh. Technol.

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Abstract-Mobile terminal location is an important area of research because of its applications in location-sensitive browsing and resource allocation. This paper presents a method for reducing the error in mobile terminal location estimation. A preprocessor using nonparametric estimation is used to reduce the effects of non-line-of-sight and multipath propagation on the location procedure. A model-based dynamic filter is presented that uses an accurate model of mobile terminal motion to combine information from location measurements made at different time instances together to create an improved location estimate. The model of mobile terminal motion has a kinematic state space model describing the physical rules governing terminal motion and a control model that describes the human control input into the motion process. Location dependency in the control input model is used to derive a new dynamic filter. This filter provides greatly improved accuracy over previously known location techniques and is much more robust to variations in the mobile terminal motion and nonlinear effects in the propagation environment.

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“…The fifth and sixth classifiers are a Parzen window classifier [13] and a K nearest neighbor (K-NN) classifier [14]. The Parzen window classifier estimates a posterior density function for each class based on a window function, the set of training vectors, and the test vector.…”

Section: Classification Systemsmentioning

confidence: 99%

Characterization of RF Devices using Two-Tone Probe Signals

Martone

Delp

2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

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This paper describes a method for forensic characterization of RF devices using two-tone probe signals. When transmitted to an RF device, the two-tone signal is affected by nonlinear circuit components such as amplifiers or diodes. The nonlinear components cause intermodulation distortion to the input signal, which is reradiated by the device. Features of the intermodulation distortion products are used to construct a device fingerprint. The fingerprint is then used to characterize the device so that it can be identified from other RF devices.

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Weighted Parzen windows for pattern classification

Cited by 97 publications

References 10 publications

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Dynamic model-based filtering for mobile terminal location estimation

Characterization of RF Devices using Two-Tone Probe Signals

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