Generalization Performance of $\ell_1$ -Coefficient Regularized Regression With Multiscale Kernels Based on Markov Chain Samples
نویسندگان
چکیده
منابع مشابه
Unified approach to coefficient-based regularized regression
In this paper, we consider the coefficient-based regularized least-squares regression problem with the lq-regularizer (1 ≤ q ≤ 2) and data dependent hypothesis spaces. Algorithms in data dependent hypothesis spaces perform well with the property of flexibility. We conduct a unified error analysis by a stepping stone technique. An empirical covering number technique is also employed in our study...
متن کاملHigh-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression
We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on `1regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an `1-constraint. Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum nei...
متن کاملregression analysis in markov chain
in a finite stationary markov chain, transition probabilities may depend on some explanatoryvariables. a similar problem has been considered here. the corresponding posteriors are derived andinferences are done using these posteriors. finally, the procedure is illustrated with a real example.
متن کاملRegularized Least Square Regression with Spherical Polynomial Kernels
This article considers regularized least square regression on the sphere. It develops a theoretical analysis of the generalization performances of regularized least square regression algorithm with spherical polynomial kernels. The explicit bounds are derived for the excess risk error. The learning rates depend on the eigenvalues of spherical polynomial integral operators and on the dimension o...
متن کاملIntroducing ℓ1-regularized logistic regression in Markov Networks based EDAs
Estimation of Distribution Algorithms evolve populations of candidate solutions to an optimization problem by introducing a statistical model, and by replacing classical variation operators of Genetic Algorithms with statistical operators, such as estimation and sampling. The choice of the model plays a key role in the evolutionary process, indeed it strongly affects the convergence to the glob...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2953527