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Title: adaptive fuzzy k mean clustering algorithm for image segmentation
Page Link: adaptive fuzzy k mean clustering algorithm for image segmentation -
Posted By: aslimath
Created at: Thursday 17th of August 2017 05:03:02 AM
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can you provide clear explanation for the formulas used in adaptive fuzzy k mean clustering algorithm for image segmentation.. n how the calculations are done??? ....etc

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Title: Grayscale Image Retrieval using DCT on Row mean Column mean and Combination
Page Link: Grayscale Image Retrieval using DCT on Row mean Column mean and Combination -
Posted By: AwarseAcrobre
Created at: Thursday 05th of October 2017 05:34:06 AM
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Grayscale Image Retrieval using DCT on Row mean,
Column mean and Combination




Abstract

Today in the age of information explosion, how to search appropriate data from huge information
pool has become vital issue. Images have giant share in this information pool. Because of easy
availability of imaging devices, millions of images are being added to image pool every day. Image
retrieval deals with searching relevant images from large image database. The paper presents novel
image retrieval techniques bas ....etc

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Title: Clustering a data set using Fuzzy K mean algorithm
Page Link: Clustering a data set using Fuzzy K mean algorithm -
Posted By: nitishrao
Created at: Thursday 17th of August 2017 08:15:57 AM
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Title: ppto mean in result rajasthan university
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Title: invalid account number what does it mean in registering online payslips tsc
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The Teachers Service Commission (TSC) has abolished manual payslips starting this month, and ordered teachers and tutors who have not registered for online payslips to do so. This means that teachers can now access their payslips on smartphones.

According to Circular signed by TSC Chief Executive Officer Nancy Macharia dated 29th June 2016 and copied to all County Directors, Principals and Head teachers, there will be no more paper payslips with effect from July 1, 2016.

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Title: Agglomerative Mean-Shift Clustering
Page Link: Agglomerative Mean-Shift Clustering -
Posted By: anoop1988
Created at: Friday 06th of October 2017 03:05:35 PM
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Abstract Mean-Shift (MS) is a powerful nonparametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. In this paper, for the purpose of algorithmic speedup, we develop an agglomerative MS clustering method along with its performance analysis. Our method, namely Agglo-MS, is built upon an iterative query set compression mechanism which is motivated by the quadratic bounding optimization nature of MS algorithm. The whole framework can be efficiently implemente ....etc

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Title: LEAST MEAN SQUARE ALGORITHM
Page Link: LEAST MEAN SQUARE ALGORITHM -
Posted By: r n midhun
Created at: Thursday 17th of August 2017 06:30:09 AM
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LEAST MEAN SQUARE ALGORITHM

Introduction
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a gradient-based method of steepest decent . LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to ....etc

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Title: image retrieval using k mean cluster source code
Page Link: image retrieval using k mean cluster source code -
Posted By: harryanand
Created at: Thursday 17th of August 2017 05:39:13 AM
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Title: absolute mean brightness error ambe matlab code
Page Link: absolute mean brightness error ambe matlab code -
Posted By: ratanroy86
Created at: Thursday 17th of August 2017 08:14:01 AM
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Title: An Affine Combination of two LMS adaptive filters transient mean square analysis
Page Link: An Affine Combination of two LMS adaptive filters transient mean square analysis -
Posted By: Shibinm
Created at: Thursday 17th of August 2017 05:12:20 AM
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An Affine Combination of two LMS adaptive filters transient mean square analysis

Abstract:
The design of many adaptive filters requires a tradeoff between convergence speed and steady-state mean-square error (MSE). A faster (slower) convergence speed yields a larger (smaller) steady-state mean-square deviation (MSD) and MSE. This property is usually independent of the type of adaptive algorithm, i.e., least mean-square (LMS), normalized least
Mean-square (NLMS), recursive least squares (RLS), or affine projection (AP).
This design tradeo ....etc

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