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Stochastic support vector regression with probabilistic constraints
- Source :
- Applied Intelligence. 48:243-256
- Publication Year :
- 2017
- Publisher :
- Springer Science and Business Media LLC, 2017.
-
Abstract
- Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, we introduce a novel model of SVR in which any training samples containing inputs and outputs are considered the random variables with known or unknown distribution functions. Constraints occurrence have a probability density function which helps to obtain maximum margin and achieve robustness. The optimal hyperplane regression can be obtained by solving a quadratic optimization problem. The proposed method is illustrated by several experiments including artificial data sets and real-world benchmark data sets.
- Subjects :
- Mathematical optimization
Structured support vector machine
Computer science
Probabilistic logic
02 engineering and technology
01 natural sciences
Probability vector
Data set
Support vector machine
Relevance vector machine
010104 statistics & probability
Hyperplane
Artificial Intelligence
Robustness (computer science)
Least squares support vector machine
Margin classifier
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Quadratic programming
0101 mathematics
Random variable
Subjects
Details
- ISSN :
- 15737497 and 0924669X
- Volume :
- 48
- Database :
- OpenAIRE
- Journal :
- Applied Intelligence
- Accession number :
- edsair.doi...........c0d5f680259a1a760c74a02917b45537