Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach

被引：0

作者：

Bellas-Velidis, Ioannis ^{[1
]}

Kontizas, Mary ^{[2
]}

Dapergolas, Anastasios ^{[1
]}

Livanou, Evdokia ^{[2
]}

Kontizas, Evangelos ^{[1
]}

Karampelas, Antonios ^{[2
]}

机构：

[1] Natl Observ Athens, Inst Astron & Astrophys, POB 20048, Athens 11810, Greece

[2] Univ Athens, Dept Astrophys Astron & Mech, Fac Phys, Athens 15783, Greece

来源：

BULGARIAN ASTRONOMICAL JOURNAL | 2012年 / 18卷 / 02期

关键词：

Methods: data analysis; Techniques: miscellaneous; Galaxies: general;

D O I：

暂无

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.

引用

页码：3 / 17

页数：15

共 50 条

[21] An Efficient Classifier Based on Hierarchical Mixing Linear Support Vector Machines
Wang, Di
Zhang, Xiaoqin
Fan, Mingyu
Ye, Xiuzi
[J]. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 3897 - 3903
[22] A hybrid classifier based on rough set theory and support vector machines
Zhang, GX
Cao, ZX
Gu, YJ
[J]. FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 1, PROCEEDINGS, 2005, 3613 : 1287 - 1296
[23] Support Vector Machines for computing action mappings in Learning Classifier Systems
Loiacono, Daniele
Marelli, Andrea
Lanzi, Pier Luca
[J]. 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, : 2141 - 2148
[24] Evolutionary Support Vector Machines: A Dual Approach
Dantas Dias, Madson Luiz
Rocha Neto, Nalsnar R.
[J]. 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 2185 - 2192
[25] Support vector machines approach to credit assessment
Li, JP
Liu, JL
Xu, WX
Shi, Y
[J]. COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS, 2004, 3039 : 892 - 899
[26] A geometric approach to train Support Vector Machines
Yang, MH
Ahuja, N
[J]. IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, VOL I, 2000, : 430 - 437
[27] An evolutionary approach to Transduction in Support Vector Machines
Silva, MM
Maia, TT
Braga, AP
[J]. HIS 2005: 5th International Conference on Hybrid Intelligent Systems, Proceedings, 2005, : 329 - 334
[28] Label-free optofluidic cell classifier utilizing support vector machines
Wu, Tsung-Feng
Mei, Zhe
Lo, Yu-Hwa
[J]. SENSORS AND ACTUATORS B-CHEMICAL, 2013, 186 : 327 - 332
[29] An effective Power Quality classifier using Wavelet Transform and Support Vector Machines
De Yong, D.
Bhowmik, S.
Magnago, F.
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (15-16) : 6075 - 6081
[30] Minimax Feature Selection Problem for Constructing a Classifier Using Support Vector Machines
Goncharov, Yu. V.
[J]. COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS, 2010, 50 (05) : 917 - 925

← 1 2 3 4 5 →