Kernel Partial Least Square Regression (KPLSR)
Short Term Electric Load Prediction by Incorporation of Kernel into Features Extraction Regression Technique
... PKPLSR: Kernel partial least square regression (KPLSR) used its technique to extract the most variance features those are drawn by polynomial kernel, while jointly maintaining ...
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Particle swarm optimized partial least square support vector regression model for tax revenue prediction
... vector regression(SVR) construct the optimal regression function through risk minimization principle to transform the problem into solving a convex quadratic programming ...the kernel function to map ...
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A Kernel Partial Least Square Based Feature Selection Method
... This paper proposes KPLS-mRMR to select strongly relevant and less redundant fea- tures based on KPLS regression coefficients. The method has been evaluated in terms of classification accuracy on seven different ...
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Partial Least Square Regression PLS-Regression
... PLS regression is to predict Y from X and to describe their common ...component regression, is to perform a principal component analysis ( PCA ) of the X matrix and then use the principal components ...
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Efficiency Improvement for Ordinary Least Square and Orthogonal Regression-An Application in Chemical Engineering
... fit regression models are constantly a challenge to the researchers, efforts are taken to minimize the error components so that the predictability and efficiency of models ...
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Kernel Affine Projection Algorithms
... As pointed out, the study of the KLMS and KAPA has a close relation with the resource-allocating networks, but in the framework of RKHS, any Mercer kernel can be used instead of restricting the architecture to the ...
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Error analysis for \(l^{q}\) coefficient regularized moving least square regression
... The least-square (LS) method is an important global approximate method based on the regular or concentrated data sample ...moving least-square (MLS) method was introduced by McLain in [4] to ...
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Applications of Least Mean Square (LMS) Algorithm Regression in Time Series Analysis
... Applications of Least Mean Square LMS Algorithm Regression in Time-Series Analysis Giovanis, Eleftherios.[r] ...
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Corrosion Inhibition Efficiency of Thiophene Derivatives on Mild Steel: A QSAR Model
... linear regression (MLR), partial least square (PLS), heuristic method (HM) and different types of artificial neural network (ANN) which can be applied for QSAR model development and can be ...
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Descriptive and multivariate analysis of microbial environmental monitoring of the hospital in the area Rharb, Kenitra (Morocco)
... linear regression (M LR) and partial least square (PLS) have been used to evaluate the pattern of the relationship among ...resuscitation. Partial least square ...
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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 ...
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Application of Fourier transform infrared (FTIR) spectroscopy coupled with multivariate calibration for quantitative analysis of curcuminoid in tablet dosage form
... with partial least square and principal component regression has been used for quantification of curcuminoid in extracts of Curcuma longa (Rohman et ...
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Detection of Plasmodium falciparum infected Anopheles gambiae using near infrared spectroscopy
... The relationship between spectra and infection was ana- lyzed using partial least square regression (PLS). Train- ing datasets were used to perform multiple leave-one-out cross validations ...
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Ultra-filtration of human serum for improved quantitative analysis of low molecular weight biomarkers using ATR-IR spectroscopy
... Mean Square Error from the validation set (RMSEV) as displayed in Figure 5 is commonly used to guide the operator in choosing the optimal number of dimensions necessary to reach the best ...
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COMPARISON OF LEAST MEDIAN SQUARE AND ORDINARY LEAST SQUARE METHODS IN THE PRESENCE OF OUTLIERS
... linear regression model is that under ideal conditions the ordinary least square performs better than other regression ...ordinary least square breaks ...ordinary least ...
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Speech Enhancement using Affine Projection Algorithm and Normalized Kernel Affine Projection Algorithm
... of kernel method is, the input data is transformed, to a high dimensional feature space through a reproducing kernel such that the innermost product operation can be computed efficiently in the feature ...
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Data driven approach to machine condition prognosis using least square regression trees
... of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting ...
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Developing non destructive techniques to predict 'Hayward' kiwifruit storability : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Palmerston North, New Zealand
... Figure 3.3 Average FF (N) as a result of preharvest manipulation of crop load and girdling (A) during the entire storage (days) period and (B) after 100 days of storage. HCG: High crop load with girdling. LCG: Low crop ...
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Partial Least Square Path analysis of knowledge, Attitude and Practice Regarding Dengue
... the Partial least square path analysis (PLS-path analysis) methodological approach can be used to improve KAP regarding dengue ...analysis. Partial least square path analysis was ...
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Data driven approach to machine condition prognosis using least square regression trees
... Machine condition prognosis is extremely significant in foretelling the degradation of working condition and trends of fault propagation before they reach the alarm. In this study, the machine prognosis system based on ...
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