Ramp Loss Linear Programming Nonparallel Support Vector Machine
نویسندگان
چکیده
منابع مشابه
Ramp loss linear programming support vector machine
The ramp loss is a robust but non-convex loss for classification. Compared with other non-convex losses, a local minimum of the ramp loss can be effectively found. The effectiveness of local search comes from the piecewise linearity of the ramp loss. Motivated by the fact that the `1-penalty is piecewise linear as well, the `1-penalty is applied for the ramp loss, resulting in a ramp loss linea...
متن کاملLarge-scale linear nonparallel support vector machine solver
Twin support vector machines (TWSVMs), as the representative nonparallel hyperplane classifiers, have shown the effectiveness over standard SVMs from some aspects. However, they still have some serious defects restricting their further study and real applications: (1) They have to compute and store the inverse matrices before training, it is intractable for many applications where data appear w...
متن کاملMonthly rainfall Forecasting using genetic programming and support vector machine
Rainfall and runoff estimation play a fundamental and effective role in the management and proper operation of the watershed, dams and reservoirs management, minimizing the damage caused by floods and droughts, and water resources management. The optimal performance of intelligent models has increased their use to predict various hydrological phenomena. Therefore, in this study, two intelligent...
متن کاملNonparallel Hyperplanes Support Vector Regressor
Motivated by nonparallel hyperplanes support vector machine (NHSVM), a new regression method of data, named as nonparallel hyperplanes support vector regression (NHSVR), is proposed in this paper. The advantages of NHSVR have two aspects, one is considering the minimization of structure risk by introducing a regularization term in objective function, and another is finding two nonparallel hyper...
متن کاملSupport vector machine classification via parameterless robust linear programming
We show that the problem of minimizing the sum of arbitrary-norm real distances to misclassified points, from a pair of parallel bounding planes of a classification problem, divided by the margin (distance) between the two bounding planes, leads to a simple parameterless linear program. This constitutes a linear support vector machine (SVM) that simultaneously minimizes empirical error of miscl...
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: Procedia Computer Science
سال: 2016
ISSN: 1877-0509
DOI: 10.1016/j.procs.2016.05.432