[PDF] Top 20 AADT prediction using support vector regression with data-dependent parameters
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AADT prediction using support vector regression with data-dependent parameters
... associated with the traffic volumes expected in the system, which means that it is important that the AADT should be accurately ...as support vector machine for regression (SVR) to ... See full document
8
Forecasting bus passenger flows by using a clustering-based support vector regression approach
... propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear ...A support vector regression (SVR) is then exploited to forecast the ... See full document
10
A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems
... training data points using one CPU processor, the parallel SMO first partitions the entire training data set into smaller subsets and then simultaneously runs multiple CPU processors to deal ... See full document
12
Forecasting the Prices of TAIEX Options by Using Genetic Programming and Support Vector Regression
... [25] with a radial basis function (RBF) kernel is utilized to implement the SVR ...for parameters C, , and in SVR are determined by the grid-search approach ...normalized data can be directly fed ... See full document
6
Prediction of labor induction outcome using different clinical parameters
... The role of body mass index and age of women, parity Bishop score, cervical length measured by transvaginal ultrasound was evaluated in regard of the success of induction, which was c[r] ... See full document
5
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
... We would like to thank the symposium presenters and participants who made this special issue possible. Special thanks go to the editors of BMC Bioinformatics who advised us in preparing the manuscripts. Finally we ... See full document
2
River flow time series using least squares support vector machines
... squares support vector machines (LSSVM), as a modification of SVM was introduced by Suykens and Van- dewalle ...quality prediction (Yunrong and Liangzhong, ... See full document
18
Pesqui. Oper. vol.35 número1
... the data structure, the features used, the extent to which it is possible to segregate the classes by using those features, and the purpose of the classification (Hand & Henley, ...logistic ... See full document
18
Likelihood-based Inference for Multivariate Regression Models using Synthetic Data
... of data synthesized via FPPS or Plug-in Sampling methods developed are more precise than the first exact pro- cedures, mainly for smaller sample sizes and they become approximately equal as the samples increase in ... See full document
110
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
... Computational Biology held at Whistler, Canada on December 18th, 2004.. This.[r] ... See full document
3
Feature Extraction based Approaches for Improving the Performance of Intrusion Detection Systems
... maintain data quality features and remove redundant and irrelevant ...combined with Partial Least Square (PLS) feature extraction technique and Core Vector Machine (CVM) algorithm to detect the ... See full document
6
An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis
... The support vector machine (SVM) algorithm [8] is a classification algorithm that provides the best performance in various application domains such as object recognition, speaker identification, face ... See full document
5
Prodepth: predict residue depth by support vector regression approach from protein sequences only.
... correlated with several protein properties, such as protein stability, residue conservation and amino acid ...Accurate prediction of RD has many potentially important applications in the field of structural ... See full document
14
USING PENALIZED REGRESSION WITH PARALLEL COORDINATES FOR VISUALIZATION OF SIGNIFICANCE IN HIGH DIMENSIONAL DATA
... transformed data based on different interpolations, and various other curves [9, 13] were also ...large data can also be uncovered by parallel coordinates [2, 9, 21, ...huge data and producing a ... See full document
7
An Efficient Prediction of Breast Cancer Data using Data Mining Techniques
... The data set consist of 699 patients’ ...the prediction results of the comparison of the six popular data mining techniques and the 10-fold crossover validation is ...of prediction models. The ... See full document
6
Information Flow Analysis using Data-dependent Logical Propositions
... tool support to ensure all needed verifications are in ...as data relevant to access control, such as user profiles and ...and with its own data ...layer, using SQL; the server-side ... See full document
113
Relations Of Peer -Victimization Exposure In Adolescents With The Perceived Social Support, Parental Attitude, School Success, School Change And Area Of Residence
... When the literature related to bullying is reviewed, it is well understood that ecological theory is very significant in explanation of the peer-victimization exposure and it is discussed in a theoretical framework. ... See full document
10
Rev. Saúde Pública vol.43 número1
... logistic regression models have shown to be suitable for analyzing data with ordinal ...up with a good explanation with a reduced number of parameters to be ...to using ... See full document
11
A Computer Aided Diagnosis System for Lung Cancer Detection Using Support Vector Machine
... image data when viewed as a relief ...out using 60 radiographs taken from a routine clinic with 90 real nodules and 288 simulated ...method with the mean number of False Positives (FP's) and ... See full document
7
Úlcera por pressão: risco e gravidade do paciente e carga de trabalho de enfermagem
... hospitalization data and the Simplified Acute Physiology Score (SAPS II), Nursing Activities Score (NAS) and Braden Scale were applied to assess patient severity, nursing workload and risk of PU, ...the ... See full document
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