Brazilian Conference on Intelligent Systems最新文献

An Incremental MaxSAT-Based Model to Learn Interpretable and Balanced Classification Rules 基于 MaxSAT 的增量模型学习可解释且平衡的分类规则

Brazilian Conference on Intelligent Systems Pub Date : 2024-03-25 DOI: 10.1007/978-3-031-45368-7_15

Antônio Carlos Souza Ferreira Júnior, Thiago Alves Rocha

引用次数: 0

Logic-Based Explanations for Linear Support Vector Classifiers with Reject Option 带拒绝选项的线性支持向量分类器的基于逻辑的解释

Brazilian Conference on Intelligent Systems Pub Date : 2024-03-24 DOI: 10.1007/978-3-031-45368-7_10

Francisco Mateus Rocha, Thiago Alves Rocha, Reginaldo Pereira Fernandes Ribeiro, A. Neto

引用次数: 0

Event Detection in Therapy Sessions for Children with Autism 自闭症儿童治疗过程中的事件检测

Brazilian Conference on Intelligent Systems Pub Date : 2023-05-03 DOI: 10.1007/978-3-031-21689-3_17

Guilherme O. Ribeiro, Alexandre Soli Soares, J. T. Carvalho, M. Grellert

引用次数: 1

Augmenting a Physics-Informed Neural Network for the 2D Burgers Equation by Addition of Solution Data Points 通过增加解数据点来增强二维Burgers方程的物理信息神经网络

Brazilian Conference on Intelligent Systems Pub Date : 2023-01-18 DOI: 10.1007/978-3-031-21689-3_28

M. Mathias, Wesley P. de Almeida, Jefferson F. Coelho, L. P. Freitas, F. M. Moreno, Caio F. D. Netto, F. G. Cozman, A. H. R. Costa, E. Tannuri, E. Gomi, M. Dottori

引用次数: 2

Single Image Super-Resolution Based on Capsule Neural Networks 基于胶囊神经网络的单幅图像超分辨率

Brazilian Conference on Intelligent Systems Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.03743

George Correa de Ara'ujo, H. Pedrini

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引用次数: 0

Explanation-by-Example Based on Item Response Theory 基于项目反应理论的举例解释

Brazilian Conference on Intelligent Systems Pub Date : 2022-10-04 DOI: 10.48550/arXiv.2210.01638

Lucas F. F. Cardoso, Joseph Ribeiro, Vitor Santos, Raíssa Silva, M. Mota, R. Prudêncio, Ronnie Alves

{"title":"Explanation-by-Example Based on Item Response Theory","authors":"Lucas F. F. Cardoso, Joseph Ribeiro, Vitor Santos, Raíssa Silva, M. Mota, R. Prudêncio, Ronnie Alves","doi":"10.48550/arXiv.2210.01638","DOIUrl":"https://doi.org/10.48550/arXiv.2210.01638","url":null,"abstract":", Abstract. Intelligent systems that use Machine Learning classiﬁcation algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have characteristics that allow for self-explanation of their predictions. This situation leads researchers in the ﬁeld and society to the following question: How can I trust the prediction of a model I cannot understand? In this sense, XAI emerges as a ﬁeld of AI that aims to create techniques capable of explaining the decisions of the classiﬁer to the end-user. As a result, several techniques have emerged, such as Explanation-by-Example, which has a few initia-tives consolidated by the community currently working with XAI. This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. To this end, four datasets with diﬀerent levels of complexity were used, and the Random Forest model was used as a hy-pothesis test. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable. Learning (ML) · Item Response Theory (IRT) · Classiﬁcation.","PeriodicalId":335206,"journal":{"name":"Brazilian Conference on Intelligent Systems","volume":"974 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132519058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Extending the Universal Approximation Theorem for a Broad Class of Hypercomplex-Valued Neural Networks 推广一类广义超复值神经网络的普遍逼近定理

Brazilian Conference on Intelligent Systems Pub Date : 2022-09-06 DOI: 10.48550/arXiv.2209.02456

Wington L. Vital, Guilherme Vieira, M. E. Valle

引用次数: 2

Optimizing Diffusion Rate and Label Reliability in a Graph-Based Semi-supervised Classifier 基于图的半监督分类器的扩散率和标签可靠性优化

Brazilian Conference on Intelligent Systems Pub Date : 2022-01-10 DOI: 10.1007/978-3-030-91702-9_34

B. Afonso, Lilian Berton

引用次数: 2

Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network 基于深度神经网络的武器交战区最大发射距离估计

Brazilian Conference on Intelligent Systems Pub Date : 2021-11-04 DOI: 10.1007/978-3-030-91699-2_14

Joao P. A. Dantas, André N. Costa, Diego Geraldo, M. Maximo, T. Yoneyama

引用次数: 7

DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment DEEPAGÉ:用葡萄牙语回答关于巴西环境的问题

Brazilian Conference on Intelligent Systems Pub Date : 2021-10-19 DOI: 10.1007/978-3-030-91699-2_29

F. N. Caccao, M. M. Jos'e, A. Oliveira, Stefano Spindola, A. H. R. Costa, Fabio Gagliardi Cozman

引用次数: 5