Previous approaches to using CBR for regression (Chapter 4)
Correlation of CBR with Index Properties of Soil of Kolar Region (Bhopal) using Regression Analysis
... of CBR with index properties of soil is specially of a Bhopal Kolar ...on regression method using NCSS software, where it is found that in case of simple linear regression analysis, six model ...
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Predicting CBR Values of Black Cotton Soil Stabilized with Cement and Waste Glass Admixture Using Regression Model
... Admixture using Multiple Regression Analysis ...(OMC)), CBR, Waste glass (WG) content and Cement content (Cm), obtained from a laboratory at Abubakar Tafawa Balewa University Bauchi, Nigeria, have ...
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Prediction of CBR Value by using Index Properties of Soil
... soaked CBR value. SLRA can be carried out by using standard statistical software like Data Analysis Tool Bar of Microsoft Excel in order to derive the relationship ...soaked CBR value predicted by ...
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Prediction of Subgrade CBR using FWD for Thin Bituminous Pavements
... develop regression models to predict the resilient modulus of subgrade soils from results of FWD backcalculated modulus for application in the design of pavement and ...
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Ensemble Approaches for Regression: A Survey
... neighbors using different distance measures, namely, the Euclidean, kd-tree [Bentley 1975], and RReliefF measures [Robnik- ˘ Sikonja and Kononenko 2003], and all examples that fall together with the test instance ...
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The Integrated Model Using Agile Practices to CBR
... professionals are rarely found. Agile methods are iterative and evolutionary ap- proach subset [8] and are based on accompanying the development process, iterative improvements and product iterations. Iteration is ...
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Lessons Learned using CBR for Customer Support
... Three CBR systems were created to improve customer support at General Electric ...by using a case base of frequent questions and their ...storing previous email with their ...
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Estimation of water quality index using artificial intelligence approaches and multi-linear regression
... 2.4. Artificial neural network (ANN) ANNs are mathematical model aims to handle non-linear relationship of input – output dataset. Historically, are information processing tools derived from analogy with biological ...
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Regression in Nonstandard Spaces with Fréchet and Geodesic Approaches
... assumptions of the general theorems are justified by showing that they are fulfilled on the sphere. The sphere is also the metric space used in our simulation study, section 8. To fulfill a variance inequality, which is ...
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New Approaches to Support Vector Ordinal Regression
... two approaches against the naive approach of us- ing standard support vector regression (SVR) and the method (SLA) of Shashua and Levin ...quantities using equal-frequency ...
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Regression-based, regression-free and model-free approaches for robust online scale estimation.
... µt−n+i ≈ µt − (n − i)βt , i = 1, . . . , n. (3) It is possible to remove the trend from the data prior to the scale estima- tion, e.g. by using robust regression estimators, resulting in ...
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Investigation of sequential experimental approaches in logistic regression modeling
... Case I: If k 0 ≥ k 1 + n n-1 runs with the desired response of 1 are needed. M 1 is the current maximum value of the variable for which the response is 1. Selecting a run which is greater than M 1 is likely to result in ...
Adaptive Robust Regression Approaches in data analysis and their Applications
... shown that the biases of the adaptive robust M-estimator is much smaller in overall. The plots are done by program D:\ZZJ\RB2006\BS_JK\SIMULATION\ADP_CASES_Update1_box_plot.sas). In summary, the results in table (4.5) ...
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Comparison of sliced inverse regression approaches for underdetermined cases
... We also proposed the CSS method that searches which submodels are the most informative to select relevant components of x. This procedure relies on a quality measure of the estimated indices, and it outperforms, in ...
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Model-based approaches to nonparametric Bayesian quantile regression
... Bayesian approaches to fully nonparametric model-based quantile ...additive regression framework with Gaussian process priors for the quantile regression func- tions and a scale uniform Dirichlet ...
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Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches
... underlying regression function, and 𝜀 n is the noise ...component regression PCA is commonly used in hydrological studies to reduce the number of variables, extract useful information and to eliminate the ...
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Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden
... possible using continuous routine monitoring data to adjust for the temporal component ...indirect approaches to assessment exposure as our LUR model and the possibility of its application in future ...
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Modelling of Engineering Thermal Problems: An Implementation using CBR with Derivational Analogy
... Seán Slattery and Pádraig Cunningham Dept of Computer Science, Trinity College, Dublin 2, Ireland . Abstract An interactive cased based reasoning tool for assisting engineers with the mathematical modelling tasks ...
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Comments on previous CBR Survey Reports of 1992 and 1996
... · South East firms more frequently obtain external advice from customers, whereas firms in Scotland, Wales and Northern England are relatively biassed towards obtaining advice from private consultants. · There is a ...
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Distributed CBR using XML
... In this section we wish to establish the idea that a dialogue with a case-based assistant can be long- lived. This can occur because the user may engage the case-based assistant with only a rough idea of his ...
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