Least square
COMPARISON OF LEAST MEDIAN SQUARE AND ORDINARY LEAST SQUARE METHODS IN THE PRESENCE OF OUTLIERS
... ordinary least square performs better than other regression ...ordinary least square breaks ...ordinary least squares (OLS) and least median squares (LMS) estimators by ...
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A Kernel Partial Least Square Based Feature Selection Method
... Maximum relevance and minimum redundancy (mRMR) has been well recognised as one of the best feature selection methods. This paper proposes a Kernel Partial Least Square (KPLS) based mRMR method, aiming for ...
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An analysis of landuse and landcover in Tiruchirappalli district using least square method
... squares statistical method.LSM (Least Square Method) is one of the techniques of Statistics. Anderson (1971) studied the land use classification using GIS. Byrant (1976) studied the interaction among the ...
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A Projected Alternating Least square Approach for Computation of Nonnegative Matrix Factorization
... The original NMF problem of Eq. (1.2) is not convex with respect to both variables W and H at the same time. But this with respect to one of factors is convex and easy to solve. Methods in Alternative nonnegative ...
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TRUST IN FINANCIAL MOBILE APPS – A PARTIAL LEAST SQUARE REGRESSION APPROACH
... overnment of India’s push towards digital transactions after demonitisation has led to flooding of financial mobile apps of banks and private corporations in the app stores. Among the panoply of financial mobile apps, ...
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Review on Mathematical Perspective for Data Assimilation Methods: Least Square Approach
... from least square to advanced filters that used in data assimilation as ...by least square method and their different versions then explains on the classical method ...
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Image De-Noising Based on Simple Total Least Square
... total least square. The simple total least square is the process of finding the smallest difference between the square of pixel and the square of 8- neighbors ...
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Least Square Privacy Preserving Technique for Intrusion Detection System
... Abstract:Network intrusion is a foremost growing concern threat in the cyberspace, which can be damage the network architecture in a multiple ways by modifying the system configuration/parameters. Hackers/Intruders are ...
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LEAST SQUARE LINEAR PHASE NON- RECURSIVE FILTER DESIGN
... work least square error design method is presented for the optimal design of FIR ...using least p-th norm shows that the ripple content disappears in a similar way but the only difference that ...
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Least Square Technique for Haze Modeling with 8-Band images
... includes a reasonable approximation for atmospheric transmittance. But still the DOS technique does not maintain a unified scattering model for haze through all bands. The least square methodology used in ...
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Face Recognition Using Fuzzy Clustering and Kernel Least Square
... 1) Extract features from each image using Fourier-Gabor filte 2) Select the best features using signal to noise ratio (SNR) 3) Delete or modify anomalous images using fuzzy c-mean cluster 4) Model the training images ...
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Design of composite digital filter with least square method parameter identification
... Parameter identification of asynchronous motor are mainly off-line identification and online identification method. Online identification method is mainly to identifystator and rotor resistance which have large time- ...
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Improved GPS Single Point Positioning Using Weighted Least Square Method
... Firstly, I would like to thank my final year project supervisor, Dr Ho Yih Hwa for his complete support in geodetic knowledge and the excellent guidance in my final year project. I was impressed with his knowledge in ...
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An opinion mining Partial Least Square Path Modeling for football betting
... Abstract. In the last few years, football betting had known a large ex- pansion in the world, using different ways to try to guess and predict the unknown in the sport. Every time, people try to prognosticate the results ...
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Frequency Estimation of Distorted Signal using Recursive Least Square Filter
... recursive least square algorithm," IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012), Nagapattinam, Tamil Nadu, 2012, ...
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Least Square Regression Learning with Data Dependent Hypothesis and Coefficient Regularzation
... I. INTRODUCTION AND MAIN RESULTS We establish in this paper the mathematical foundations of least square regression learning with general kernel and coefficient regularization. Let X ⊂ ℜ q , Y ⊂ ℜ be Borel ...
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Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model
... In this paper, comparative study of two CCIR pathloss model tuning approaches is presented. Both methods are least square methods. The first model tuning approach is implemented by the addition or ...
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Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model
... However, for more accurate estimation of pathloss in any given terrain, empirical pathloss models are usually subjected to empirical prediction performance evaluation and further or optimization with respect to field ...
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Reversible data hiding using least square predictor via the LASSO
... On the other hand, various papers [14 – 16] focused on improving PSNR performance in small embedding capacity. Dragoi and Coltuc [16] utilized the rhombus pattern even in small embedding capacity and obtain a good ...
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A Steerable Least Square Approach for Pattern Synthesis
... traditional least square ...traditional least square method, the new approach does not increase computational complexity when P and D are with the same ...
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