The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Neural Computation
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint time-frequency-space classification of EEG in a brain-computer interface application
EURASIP Journal on Applied Signal Processing
A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Interval regression analysis using support vector networks
Fuzzy Sets and Systems
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Twin least squares support vector regression
Neurocomputing
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In this paper, an efficient twin parametric insensitive support vector regression (TPISVR) is proposed. The TPISVR determines indirectly the regression function through a pair of nonparallel parametric-insensitive up- and down-bound functions solved by two smaller sized support vector machine (SVM)-type problems, which causes the TPISVR not only have the faster learning speed than the classical SVR, but also be suitable for many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value. The proposed method has the advantage of using the ratio of the parameters @n and c for controlling the bounds of fractions of support vectors and errors. The experimental results on several artificial and benchmark datasets indicate that the TPISVR not only has fast learning speed, but also shows good generalization performance.