2017 Brazilian Conference on Intelligent Systems (BRACIS)最新文献

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PELESent: Cross-Domain Polarity Classification Using Distant Supervision PELESent:使用远程监督的跨域极性分类
2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-07-09 DOI: 10.1109/BRACIS.2017.45
Edilson Anselmo Corrêa Júnior, V. Q. Marinho, L. B. D. Santos, Thales Bertaglia, Marcos Vinícius Treviso, H. Brum
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引用次数: 11
Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever 基于高斯过程的登革热时空建模的高效推理
2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-06-28 DOI: 10.1109/BRACIS.2017.13
J. Albinati, Wagner Meira Jr, G. Pappa, A. Wilson
{"title":"Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever","authors":"J. Albinati, Wagner Meira Jr, G. Pappa, A. Wilson","doi":"10.1109/BRACIS.2017.13","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.13","url":null,"abstract":"Dengue fever is a disease that affects hundreds of millions of people every year worldwide. Despite its wide presence around the world, it still requires accurate early warning systems. In this paper, we propose an accurate model to forecast dengue fever incidence at hundreds of Brazilian cities simultaneously. In order to assure efficiency, we devise two strategies to reduce computational effort required for inference under the proposed model. As a result, we not only reduce the computational effort that would be required to fit each model per city, but also increase the accuracy by inducing spatial dependences between cities. These dependences do not require human specification and are learned from data, leading to more accurate predictions than using typical neighborhood or distance-based methods.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121935461","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
Acoustic Modeling Using a Shallow CNN-HTSVM Architecture 基于CNN-HTSVM浅层结构的声学建模
2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-06-27 DOI: 10.1109/BRACIS.2017.62
C. Shulby, M. D. Ferreira, R. Mello, S. Aluísio
{"title":"Acoustic Modeling Using a Shallow CNN-HTSVM Architecture","authors":"C. Shulby, M. D. Ferreira, R. Mello, S. Aluísio","doi":"10.1109/BRACIS.2017.62","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.62","url":null,"abstract":"High-accuracy speech recognition is especially challenging when large datasets are not available. It is possible to bridge this gap with careful and knowledge-driven parsing combined with the biologically inspired CNN and the learning guarantees of the Vapnik Chervonenkis (VC) theory. This work presents a Shallow-CNN-HTSVM (Hierarchical Tree Support Vector Machine classifier) architecture which uses a predefined knowledge-based set of rules with statistical machine learning techniques. Here we show that gross errors present even in state-of-the-art systems can be avoided and that an accurate acoustic model can be built in a hierarchical fashion. The CNNHTSVM acoustic model outperforms traditional GMM-HMM (Gaussian Mixture Model - Hidden Markov Model) models and the HTSVM structure outperforms a MLP multi-class classifier. More importantly we isolate the performance of the acoustic model and provide results on both the frame and phoneme level, considering the true robustness of the model. We show that even with a small amount of data, accurate and robust recognition rates can be obtained.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131715915","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: 4
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