Multivariate regression model with constraints
نویسندگان
چکیده
منابع مشابه
Multivariate Regression via Stiefel Manifold Constraints
We introduce a learning technique for regression between highdimensional spaces. Standard methods typically reduce this task to many onedimensional problems, with each output dimension considered independently. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constrai...
متن کاملMultivariate Regression with Calibration
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...
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 کاملModel selection in regression under structural constraints
The paper considers model selection in regression under the additional structural constraints on admissible models where the number of potential predictors miht be even larger than the available sample size. We develop a Bayesian formalism which is used as a natural tool for generating a wide class of model selection criteria based on penalized least squares estimation with various complexity p...
متن کاملMultitarget Polynomial Regression with Constraints
The paper addresses the task of multi-target polynomial regression, i.e., the task of inducing polynomials that can predict the value of more then one numeric variable. As in other learning tasks, we face the problem of finding an optimal trade-off between the complexity of the induced model and its predictive error. We propose a minimal description length scheme for multi-target polynomial reg...
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: Mathematica Slovaca
سال: 2007
ISSN: 1337-2211,0139-9918
DOI: 10.2478/s12175-007-0022-7