title: Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð´Ð²ÑÑ sv-Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Comparison of the effectiveness of two LS_SVR algorithms
Creators: name: ÐÑÑÑÐ·Ð¾Ð²Ð°, ÐÑÐ¸ÑÑÐ¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð° Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð²
Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ðµ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸
ÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð°Ñ ÑÑÐ½ÐºÑÐ¸Ñ Ð¿Ð¾ÑÐµÑÑ
ÑÐ°Ð·ÑÐµÐ¶ÐµÐ½Ð½Ð¾Ðµ Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð¸Ðµ ÐºÐ¾ Ð²ÑÑÐ´Ñ Ð¿Ð»Ð¾ÑÐ½Ð¾Ð¼Ñ ÑÐµÑÐµÐ½Ð¸Ñ
krls-Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
gsls-Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
support vector machine
regression recovery
quadratic loss function
sparse least squares support vector machines
algorithm krls
algorithm gsls

description: ÐÑÐµÐ´Ð¼ÐµÑ ÐÐÐ : Ð·Ð°Ð´Ð°ÑÐ° Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ Ñ ÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸ÐµÐ¹ Ð¿Ð¾ÑÐµÑÑ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ ÐµÐµ ÑÐµÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½ Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð². Ð¦ÐµÐ»Ñ ÐÐÐ : ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð´Ð²ÑÑ SV-Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸: Kernel Recursive Least Squares Algorithm (KRLS) Ð¸ Greedy Training Algorithm for Sparse Least Squares Support Vector Machines (GSLS). ÐÐµÑÐ¾Ð´Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÐÐÐ : ÑÐ°Ð±Ð¾ÑÐ° Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð² ÑÐµÐ±Ñ Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² KRLS Ð¸ GSLS, ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð² ÑÑÐµÐ´Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Matlab, Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÑÐ°Ð±Ð¾ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»ÑÐ½ÑÑ Ð¸ ÑÐµÐ°Ð»ÑÐ½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÐÐÐ : Ð² Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð¹ ÑÐ°ÑÑÐ¸ ÑÐ°Ð±Ð¾ÑÑ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¾ÑÐ½Ð¾Ð²Ñ Ð´Ð»Ñ SVM Ð·Ð°Ð´Ð°Ñ Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ Ñ ÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸ÐµÐ¹ Ð¿Ð¾ÑÐµÑÑ, Ð¾Ð¿Ð¸ÑÐ°Ð½Ñ Ð±Ð°Ð·Ð¾Ð²ÑÐµ Ð¸Ð´ÐµÐ¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð², Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ ÑÑÐ°Ð¿Ñ, Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸. ÐÐ±Ð»Ð°ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ: SVM-Ð¿Ð¾Ð´ÑÐ¾Ð´ Ðº ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ Ñ ÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸ÐµÐ¹ Ð¿Ð¾ÑÐµÑÑ ÑÐ¸ÑÐ¾ÐºÐ¾ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼ Ð½Ð° Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ, Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, Ð¿ÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð², Ð¿ÑÐ¸ ÑÐ¸Ð¼Ð¸ÑÐµÑÐºÐ¾Ð¼ Ð°Ð½Ð°Ð»Ð¸Ð·Ðµ Ð²ÐµÑÐµÑÑÐ², Ð² Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐµ. ÐÑÐ²Ð¾Ð´Ñ: Ð¾Ð±Ð° SV-Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, KRLS Ð¸ GSLS, ÑÑÑÐ¾ÑÑ ÑÐ¾ÑÐ¾ÑÐµÐµ, ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾Ðµ Ðº ÑÑÐ¼Ñ, ÑÐ°Ð·ÑÐµÐ¶ÐµÐ½Ð½Ð¾Ðµ Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð¸Ðµ ÐºÐ¾ Ð²ÑÑÐ´Ñ Ð¿Ð»Ð¾ÑÐ½Ð¾Ð¼Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ Ñ ÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸ÐµÐ¹ Ð¿Ð¾ÑÐµÑÑ.
description: The subject: the problem of regression recovery with a quadratic loss function and methods for it sparse solving. The purpose: a comparative analysis of the effectiveness of two SV regression algorithms: Kernel Recursive Least Squares Algorithm (KRLS) and Greedy Training Algorithm for Sparse Least Squares Support Vector Machines (GSLS). Methodologies: the work includes the study of KRLS and GSLS algorithms, the implementation of algorithms in the Matlab programming environment, analysis of the work results of implemented methods on model and benchmark data, comparison of the methods. Results: in the main part of the work, the theoretical foundations for SVM regression problems with a quadratic loss function are considered, basic ideas of the algorithms, their main stages are described, the work results of the implemented methods are demonstrated, a comparative analysis of the effectiveness is carried out. Application field: SVM approach to solving the regression problem with a quadratic loss function is widely applicable in practice, for example, in predicting time series, in chemical analysis of substances, in bioinformatics. Conclusions: Both SV-algorithms, KRLS and GSLS, build a good, noise-resistant, sparse approximation for an everywhere dense solution of the regression reconstruction problem with a quadratic loss function.