Support Vector Regression with Interval-Input Interval-Output
نویسندگان
چکیده
منابع مشابه
Support Vector Regression with Interval-Input Interval-Output
Support vector machines (classification and regression) are powerful machine learning techniques for crisp data. In this paper, the problem is considered for interval data. Two methods to deal with the problem using support vector regression are proposed and two new methods for evaluating performance for estimating prediction interval are presented as well.
متن کاملSupport Vector Machine for Interval Regression
Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity mode...
متن کاملSupport vector regression with random output variable and probabilistic constraints
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...
متن کاملInterval Regression Analysis with Reduced Support Vector Machine
The support vector machine (SVM) has been widely used in pattern recognition, regression and distribution estimation for crisp data. However, when dealing with large-scale data sets, the solution by using SVM with nonlinear kernels may be difficult to find. Under this circumstance, to develop an efficient method is necessary. Recently the reduced support vector machine (RSVM) was proposed as an...
متن کاملMultiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furtherm...
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: International Journal of Computational Intelligence Systems
سال: 2008
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2008.9727626