regression algorithm
Using Logic Regression Algorithm to Analyze and Predict NBA Game Data
... There are 82 games for each team. In each field, the result of the results of each game is predicted by using the logic regression algorithm. Suppose that our sample is {x, y}, y is 0 or 1, representing a ...
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An incremental dual nu-support vector regression algorithm
... vector regression (SVR) has been a hot research topic for several years as it is an effective regression learning ...incremental regression problem with uncertain data, an incremental dual nu-support ...
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Development of a Prediction Model for Nigerian Stock Exchange using Linear Regression Algorithm
... learning algorithm after the historical dataset is trained with the ...linear regression algorithm and the coefficients and intercept of the model were ...
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Performance Analysis of a Novel Robust Support Vector Regression Algorithm
... Performance Analysis of a Novel Robust Support Vector Regression Algorithm YUAN LV, CHAO XU and BIN ZHONG ABSTRACT The experimental data with polyhedral perturbation is a huge challenge [r] ...
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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
... Based on the results obtained, then got the following conclusion: Based on the comparison of RWO-Sampling method with roulette approach, RWO-Sampling with random replicate and Random Oversampling (ROS) approach proved ...
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Analysis on Regression Algorithm with Surf Feature and SVM by Emotion Detection
... ABSTRACT: One of the major concerns in the field of computer vision and pattern recognition is emotion detection. One difficulty in face recognition is how to handle the variations in the expression, pose and ...
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An iterative orthogonal forward regression algorithm
... Forward Regression (OFR) algorithm have been widely applied in nonlinear system identification including in the modelling of many engineering, chemical, biological, medical, geographical, and economic ...
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Bayesian regression algorithm and its modification with application to public health data
... Amir, Wan Muhamad; Shafiq, Mohamad; A.Rahim, Hanafi; Liza, Puspa; Aleng, Azlida; and Abdullah, Zailani, 2016. "Algorithm for Combining Robust and Bootstrap In Multiple Linear Model Regression ...
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An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... subset selection cost function includes a D-optimality de- sign criterion. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ...
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The enhancement of Linear Regression algorithm in handling missing data for medical data set
... linear regression model as a selected imputation method in ...linear regression model which named as LReHalf, in order to produce high quality data cleaning ...linear regression model ...
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An extended orthogonal forward regression algorithm for system identification using entropy
... OFR algorithm is shown in Table 5, which indicates that only 7 out of 66 candidate terms are selected to yields a very simple nonlinear ...new algorithm are: µ 3 = − ...
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Local attribute-similarity weighting regression algorithm for interpolating soil property valuesLocal attribute-similarity weighting regression algorithm for interpolating soil property values
... Noting a spatially measured point normally contains both spatial attributes (geographical coordinates) and non-spatial attributes (such as environmental factor and other properties determined by physical or chemical ...
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Prediction of Agro Products Sales Using Regression Algorithm
... using regression models for prediction is usually based on the assumption that the prospective value of a variable is linked to its past values ...using regression for prediction is to uncover patterns in ...
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A forward regression algorithm based on M estimators
... proposed algorithm in this work is a variant of modified Gram-Schmidt procedure that includes the IRLS inner loop so as to derive the M- estimators of the auxiliary vector ...
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Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery
... ACO-based regression algorithm in order to extract a list of monotonic regres- sion ...proposed algorithm against a greedy regression rule induction algorithm that preserves monotonic ...
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Ridge Regression Learning Algorithm in Dual Variables
... non-linear regression methods; see, e.g., Breiman [1] and Saunders [6]. The data set consists of 506 cases in which 12 continuous variables and 1 binary vari- able determine the median house price in a certain ...
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A Predictive Model for Mortality of Patients with Thalassemia Using Logistic Regression Model and Genetic Algorithm
... logistic regression algorithm was applied on all features in the first method and a model was designed based on the obtained ...genetic algorithm on the data, followed by designing and evaluating the ...
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Three-structured smooth transition regression models based on CART algorithm
... model that can be analyzed either as STR model or as a fuzzy regression (Jajuga 1986). In the former case, we can obtain confidence intervals for the parameters estimates in the tree leaves and predicted values. ...
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Misspecified Multivariate Regression Models Using the Genetic Algorithm and Information Complexity as the Fitness Function
... genetic algorithm to consistently iden- tify the known true subset regression model in the presence of multicolinearity, unnecessary variables, redundant variables, and asymmetrical or leptokurtic ...
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