Audio feature optimization approach towards speaker authentication in banking biometric system

2021 ◽  
Vol 150 (4) ◽  
pp. A349-A349
Author(s):  
Szymon Zaporowski ◽  
Andrzej Czyzewski ◽  
Bozena Kostek
Keyword(s):  
Optimization Approach ◽  
Biometric System ◽  
Audio Feature ◽  
Feature Optimization ◽  
Speaker Authentication

A Model-based Feature Optimization Approach to Chinese Language Processing

2014 ◽  
Vol 22 (1) ◽  
pp. 55-81
Author(s):  
Yuting Yang ◽  
Yunhua Qu ◽  
Chenyao Bao ◽  
Xiaowen Zhang
Keyword(s):  
Language Processing ◽  
Chinese Language ◽  
Optimization Approach ◽  
Model Based ◽  
Feature Optimization

scholarly journals A Feature Optimization Approach Based on Inter-Class and Intra-Class Distance for Ship Type Classification

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5429
Author(s):  
Chen Li ◽  
Ziyuan Liu ◽  
Jiawei Ren ◽  
Wenchao Wang ◽  
Ji Xu
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
State Of The Art ◽  
Optimization Method ◽  
Classification Algorithms ◽  
Optimization Approach ◽  
Time Frequency ◽  
Real Environment ◽  
Feature Optimization ◽  
Type Classification

Deep learning based methods have achieved state-of-the-art results on the task of ship type classification. However, most existing ship type classification algorithms take time–frequency (TF) features as input, the underlying discriminative information of these features has not been explored thoroughly. This paper proposes a novel feature optimization method which is designed to minimize an objective function aimed at increasing inter-class and reducing intra-class feature distance for ship type classification. The objective function we design is able to learn a center for each class and make samples from the same class closer to the corresponding center. This ensures that the features maximize underlying discriminative information involved in the data, particularly for some targets that usually confused by the conventional manual designed feature. Results on the dataset from a real environment show that the proposed feature optimization approach outperforms traditional TF features.

scholarly journals Production of the Japan 30-m Land Cover Map of 2013–2015 Using a Random Forests-Based Feature Optimization Approach

2016 ◽  
Vol 8 (5) ◽  
pp. 429 ◽  
Author(s):  
Ram Sharma ◽  
Ryutaro Tateishi ◽  
Keitarou Hara ◽  
Kotaro Iizuka
Keyword(s):  
Land Cover ◽  
Random Forests ◽  
Optimization Approach ◽  
Land Cover Map ◽  
Feature Optimization

Interactive multiobjective DEA target setting using lexicographic DDF

2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano
Keyword(s):  
Efficient Frontier ◽  
Directional Distance Function ◽  
Fixed Number ◽  
Optimization Approach ◽  
Target Setting ◽  
Interactive Process ◽  
Decision Making Unit ◽  
Setting Approach ◽  
Promising Area ◽  
Current Region

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).

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