Fixed Parameters Support Vector Regression for Outlier Detection
نویسندگان
چکیده
منابع مشابه
Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator
The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...
متن کاملSupport Vector Clustering for Outlier Detection
In this paper a novel Support vector clustering(SVC) method for outlier detection is proposed. Outlier detection algorithms have application in several tasks such as data mining, data preprocessing, data filter-cleaner, time series analysis and so on. Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only prefe...
متن کاملOutlier Detection by Boosting Regression Trees
A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...
متن کاملSupport vector regression for prediction of gas reservoirs permeability
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of pe...
متن کاملSelection of Meta-parameters for Support Vector Regression
We propose practical recommendations for selecting metaparameters for SVM regression (that is, ε -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
سال: 2018
ISSN: 0424-267X,1842-3264
DOI: 10.24818/18423264/52.2.18.16