A comparative study of the use of large margin classifiers on seismic data

被引：0

作者：

Drosou, Krystallenia ^{[1
]}

Artemiou, Andreas ^{[2
]}

Koukouvinos, Christos ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Dept Math, GR-15773 Athens, Greece

[2] Cardiff Univ, Sch Math, Cardiff CF24 4AG, S Glam, Wales

来源：

JOURNAL OF APPLIED STATISTICS | 2015年 / 42卷 / 01期

关键词：

reweighted methods; real-time problems; proximal SVM; SVM; large-scale classification; incremental procedure; SUPPORT VECTOR MACHINES;

D O I：

10.1080/02664763.2014.938619

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this work we present a study on the analysis of a large data set from seismology. A set of different large margin classifiers based on the well-known support vector machine (SVM) algorithm is used to classify the data into two classes based on their magnitude on the Richter scale. Due to the imbalance of nature between the two classes reweighing techniques are used to show the importance of reweighing algorithms. Moreover, we present an incremental algorithm to explore the possibility of predicting the strength of an earthquake with incremental techniques.

引用

页码：180 / 201

页数：22

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