The effective use of the One-Class SVM classifier for reduced training samples and its application to handwritten signature verification

Guerbai, Yasmine; Chibani, Youcef; Hadjadji, Bilal

doi:10.1109/icmcs.2014.6911221

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…The hyperparameters optimized for training are the penalty parameter of the error term ( C ), and the Gamma parameter (

). One-class SVM is shown to be the version of SVM that detects rare events more sufficiently [ 30 , 31 ]. To validate that, we tested our data with this model since the negative class might not be deterministic as stated previously in Section 2.3 .…”

Section: Methodsmentioning

confidence: 99%

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Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Al-Aamri

Kudlicki

Maalouf

et al. 2023

Biology

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Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

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“…The hyperparameters optimized for training are the penalty parameter of the error term ( C ), and the Gamma parameter (

Section: Methodsmentioning

confidence: 99%

“…One-class SVM is shown to be the version of SVM that detects rare events more sufficiently [ 30 , 31 ]. To validate that, we tested our data with this model since the negative class might not be deterministic as stated previously in Section 2.3 .…”

Section: Methodsmentioning

confidence: 99%

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Al-Aamri

Kudlicki

Maalouf

et al. 2023

Biology

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show abstract

“…We compare our AVN method with several other approaches: Morphology [ [62], Graph Matching [58], SigNet-F [39], OC-SVM [63], HDLSC [17], PDSN [64], Triplet Nets-Graph [65], OSIVCN [66], Ensemble Learning [67] and HOCCNN [68]. Table I shows the experiment results of different approaches.…”

Section: B Cedar Datasetmentioning

confidence: 99%

AVN: An Adversarial Variation Network Model for Handwritten Signature Verification

Wei

2022

IEEE Trans. Multimedia

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Handwritten signature verification is a crucial yet challenging problem. While previous studies have made great progress in this problem, they learn signature features passively from given existing data. In this paper, we propose a novel adversarial variation network (AVN) model for handwritten signature verification which mines effective features by actively varying existing data and generating new data. Powered by a proposed novel variation consistency mechanism, the AVN contains three different types of modules unified under one end-to-end framework: the extractor seeks to extract deep discriminative features of handwritten signatures, the discriminator aims to make verification decisions based on the extracted features, and the variator is designed to actively generate signature variants for constructing a more discriminative model. The proposed model is trained in an adversarial way with a min-max loss function, by which the three modules cooperate and compete to enhance the entire model's ability and therefore the signature verification performance is improved. We test the proposed method on four challenging signature datasets of different languages: CEDAR, BHSig-Hindi, BHSig-Bengali, and GPDS Synthetic Signature. Extensive experiments with in-depth discussions demonstrate the strength and potential of the proposed method.

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“…Já o one-class SVM (OCSVM) é um algoritmo de classificação de apenas uma classe, sendo uma adaptação proposta por Schölkopf et al (2001). O conceito do OCSVM consiste em encontrar uma hiperesfera em que a maioria das amostras de treinamento estão incluídas em um volume mínimo (Guerbai et al, 2014).…”

Section: Máquina De Vetores De Suporteunclassified

Detecção de anomalias em poços de petróleo surgentes com stacked autoencoders

Nascimento

Barbosa

Vargas

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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The effective use of the One-Class SVM classifier for reduced training samples and its application to handwritten signature verification

Cited by 9 publications

References 15 publications

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

AVN: An Adversarial Variation Network Model for Handwritten Signature Verification

Detecção de anomalias em poços de petróleo surgentes com stacked autoencoders

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