Parallel Efficient Mesh Deformation Method Based On Support Vector Regression
نویسندگان
چکیده
منابع مشابه
Efficient Structured Support Vector Regression
Support Vector Regression (SVR) has been a long standing problem in machine learning, and gains its popularity on various computer vision tasks. In this paper, we propose a structured support vector regression framework by extending the max-margin principle to incorporate spatial correlations among neighboring pixels. The objective function in our framework considers both label information and ...
متن کاملA multiwavelet support vector regression method for efficient reliability assessment
As a new sparse kernel modeling technique, support vector regression has become a promising method in structural reliability analysis. However, in the standard quadratic programming support vector regression, its implementation is computationally expensive and sufficient model sparsity cannot be guaranteed. In order to mitigate these difficulties, this paper presents a new multiwavelet linear p...
متن کاملRegression Based on Support Vector Classification
In this article, we propose a novel regression method which is based solely on Support Vector Classification. The experiments show that the new method has comparable or better generalization performance than ε-insensitive Support Vector Regression. The tests were performed on synthetic data, on various publicly available regression data sets, and on stock price data. Furthermore, we demonstrate...
متن کاملSupport vector regression based on data shifting
In this article, we provide some preliminary theoretical analysis and extended practical experiments of a novel regression method proposed recently which is based on representing regression problems as classification ones with duplicated and shifted data. The main results regard partial equivalency of Bayes solutions for regression problems and the transformed classification ones, and improved ...
متن کاملMultiscale Support Vector Regression Method On Spheres with Data Compression
In this manuscript, we investigate the multiscale support vector regression (SVR) method with data compression for approximation of functions on the unit sphere. The data are obtained at scattered sites on the sphere and may contain noise. The Vapnik ε-intensive loss function, which has been well-developed in learning theory, is introduced to obtain a local regularized approximation at each ste...
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: Applied Science and Innovative Research
سال: 2020
ISSN: 2474-4980,2474-4972
DOI: 10.22158/asir.v4n4p54