Performance of the proposed algorithm on a linear regression problem: (a)
Performance Analysis of Quickreduct, Quick Relative Reduct Algorithm and a New Proposed Algorithm
... Reduct Algorithm and a New Proposed Algorithm Ashima Gawar Abstract- Feature Selection is a process of selecting a subset of relevant features from a huge dataset that satisfy method dependent ...
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The enhancement of Linear Regression algorithm in handling missing data for medical data set
... common problem faced by researchers in many ...this problem and multiple imputation technique is the best ...a linear regression model (LR) as the selected imputation model, and ...
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Performance Analysis of Data Encryption Standard Algorithm & Proposed Data Encryption Standard Algorithm
... the performance, the algorithm has been tested on an Intel based machine running Microsoft Windows 7 with a ...The algorithm is developed on JAVA with Net Beans 6.9 software. The proposed ...
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Optimal Piecewise Linear Regression Algorithm for QSAR Modelling
... The selection of a subset of features that is most rele- vant to the prediction problem is an important strategy towards more interpretable QSAR models. A modeller can select these features empirically, according ...
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A Proposed Nth – Order Jackknife Ridge Estimator for Linear Regression Designs
... Dept. of Mathematics, Federal University Lafia, Nigeria. e-mail address: mbe_nja@yahoo.com Abstract Several remediation measures have been developed to circumvent the problem of collinearity in General ...
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An algorithm for the estimation of a regression function by continuous piecewise linear functions
... piecewise linear functions, over which we minimize an empirical least squares ...piecewise linear function can be represented as a max- ima of minima of linear functions [5, ?], we use such a ...
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An Incremental Sparse Linear Regression Classification Algorithm for Face Recognition
... Linear regression. Abstract. This paper mainly aims at improving the performance of Linear Regression Classification(LRC) when facing large scale training ...Sparse Linear ...
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Estimation of bivariate linear regression data via Jackknife algorithm
... generalization performance of predictive ...the problem is to estimate the quality of the prediction for new ...the performance of the predictive model as a random effect model. The problem of ...
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The Watermelon Algorithm for The Bilevel Integer Linear Programming Problem
... each algorithm, three time values are reported from top to bottom: how long it took to find the first bilevel feasible solution, to find the optimal solution, and to confirm its ...average performance over ...
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Solving Mining Aspects Problem by Non Linear Regression Technique and FIRS
... we proposed a Soft Computing tool FIRS for data mining for handling issues related to incomplete/imprecise data/query, fuzzy approach for weighted queries to fulfill the user information ...
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A Fast Algorithm for the High Order Linear and Nonlinear Gaussian Regression filter
... Gaussian regression filter, including both the linear and nonlinear filter of zeroth, second order, has been ...fast algorithm based on the FFT algorithm has been proposed and tested ...
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JMASM 46: Algorithm for Comparison of Robust Regression Methods In Multiple Linear Regression By Weighting Least Square Regression (SAS)
... model Y = X1 X2 X3 X4 X5 X6 X7 X8 / diagnostics leverage; run; Results Compiled in Table 2 are the results from the multiple regression analysis using the original data. Compiled in Table 3 are the results for ...
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A two level domain decomposition algorithm for linear complementarity problem
... complementarity problem (LCP) is ...the proposed algorithm. The algorithm is proved to be convergent and can reach the solution of the problem within finite ...
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Understanding the Performance of Statistical MT Systems: A Linear Regression Framework
... 1 Introduction In their strive to improve machine translation, researchers constantly introduce new models and features; new training and decoding algorithms, or experiment with variations and combination of existing ...
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Introduction to Linear Regression
... academic performance indicated that they had not done as well as expected on the ...The problem with the study is that the method of selecting students likely resulted in a disproportionate number of ...
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Robust linear regression
... this problem, bounded influence generalized M estimate (GM estimate) were proposed to produce stable results when there are outliers in the explanatory ...
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Development of a Prediction Model for Nigerian Stock Exchange using Linear Regression Algorithm
... 2017) Proposed the use of soft computing techniques to predict the future trend of stock markets using an ANN model ...Multiple Linear Regression and the Artificial Neural Network and concluded that ...
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Improvement of the branch and bound algorithm for solving the knapsack linear integer problem
... knapsack problem is NP hard [10, 18, 19, 26, 35, 36] and an optimal solution is very difficult to ...parallel algorithm for solving the NP-complete Knap- sack Problem was ...knapsack problem ...
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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
... classification algorithm used is Neural ...the performance of the proposed method using G-mean and AUC and the validation method used is 5-fold ...research proposed for this is to handle data ...
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Linear Support Vector Regression with Linear Constraints
... optimization problem related to the ...optimization problem has a closed form solution whereas the NNLS uses sophisticated algorithms [4] to approach the ...optimization problem into sub-problems ...
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