[PDF] Top 20 Finding kernel function for stock market prediction with support vector regression
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Finding kernel function for stock market prediction with support vector regression
... Modeling a forecasting system is widely discussed and studied for years, especially the topic about the trend analysis on time series or index series in which both of series definitely can be represented as a single ... See full document
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A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction
... Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied research- ...the ... See full document
22
Indonesia Composite Index Prediction using Fuzzy Support Vector Regression with Fisher Score Feature Selection
... membership function on his first paper about fuzzy sets ...membership function is a curve showing the mapping of data input points into intervals ...membership function is a curve that shows the ... See full document
8
Prediction of Transmitted Wave Height of Tandem Breakwater using Support Vector Regression
... Graphs were plotted for measured values vs predicted values with the results obtained using SVR for different kernel functions, for comparing all the results in similar manner a 45 o line was drawn in all the ... See full document
6
Support Vector approach by using Radial Kernel Function for Prediction of Software Maintenance Effort on the basis of Multivariate Approach
... in prediction process can be undertaken. The prediction of software maintenance effort is calculated by ...Radial kernel function for the regression value calculation of each ... See full document
5
Prediction of the Moving Direction of Google Inc. Stock Price Using Support Vector Classification and Regression
... a stock market is still a big challenging task ...that stock market is noisy, chaotic, nonparametric and non-linear in nature, and many external entities like politics, human ... See full document
14
Protein binding affinity prediction using support vector regression and interfecial features
... E. Performance Evaluation and Hyper-parameter selection The proposed models were imp le mented in Python 2.7. We have used leave-one-out cross validation (LOOCV) for evaluating our mach ine learn ing models. In LOOCV, a ... See full document
6
Particle Swarm Optimization With Kernel Support Vector Machine For Churn Prediction In Telecommunication Industry
... on market, several telecommunications firms apply the use of data mining (DM) models which is helpful in predicting churn user ...of finding a client with churn idea or ...churn prediction method, ... See full document
5
Parsimonious support vector machine regression using orthogonal forward selection with the generalized kernel model
... sparse kernel modeling techniques, such as the relevant vector machine and support vector machine (SVM) [2,3], have been gaining popularity in data modeling applications especially SVM ...In ... See full document
9
A Fast And Energy Efficient Path Planning Algorithm For Offline Navigation Using Svm Classifier
... Nowadays finding a path comes in handy using navigation software like maps through GPS ...compute. Support Vector Machine (SVM) algorithm is implemented as a machine learning technique ...and ... See full document
5
Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods
... Support Vector (SV) machines and related kernel-based algorithms are modern learning systems motivated by results of statistical learning theory as introduced by Vapnik ...a prediction ... See full document
18
Copper Price Prediction Using Support Vector Regression Technique
... a prediction of the closing value of copper in the Metal London Stock Exchange, being the RMSE equal or less than the ...for prediction periods of 5 and 10 days. The method consists of finding ... See full document
10
Analysis of Support Vector Regression Model for Micrometeorological Data Prediction
... series prediction is support vector regression ...the support vector machine ...linear regression by considering the width of the margin of the regression ...a ... See full document
12
Prediction of Tanzanian Energy Demand using Support Vector Machine for Regression (SVR)
... The support vector machines (SVMs) in machine learning are supervised learning models with associated learning algorithms that analyze data and recognize patterns ...for regression applies a loss ... See full document
6
Water Demand Prediction using Artificial Neural Networks and Support Vector Regression
... the support vector machines ...different kernel functions and some of these kernel functions had additional arguments such as the degree and the ...the Support Vector Genius ... See full document
8
Stock Market Prediction Using Support Vector Machine
... Financial Market Rising share prices, for instance, tend to be associated with increased business investment and vice ...the stock market and, in general, on the smooth operation of financial system ... See full document
8
Support Vector Machines for Prediction of Futures Prices in Indian Stock Market
... introduced support vector machine based on structural risk minimization principle ...[4]. Support vector machine successfully overcame the defects as a result predictive accuracy is improved ... See full document
5
Seasonal prediction of winter extreme precipitation over Canada by support vector regression
... nonlinear regression problems, whereas kernel methods (e.g. support vector re- gression, SVR) arrived in a second wave in the second half of the 1990s (Bishop, 2006; Hsieh, ...nonlinear ... See full document
10
Landslide susceptibility modeling based on GIS and novel bagging-based Kernel logistic regression
... the prediction capability of all co n d itio n in g factors w as ev alu ated u sin g th e least squ are su p p o rt v ecto r m achin e m ethod ...ark support vector m achine ...the prediction ... See full document
19
Data-Adaptive Kernel Support Vector Machine
... on kernel functions (Hastie et al. (2001)). Hence, it is the kernel that is crucial to determine the performance of the SVM ...optimal kernel function is driven by the prior knowledge of the ... See full document
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