International Conference on Machine Learning and Computing最新文献_第5页

Evolutionary Convolutional Neural Networks Using ABC 基于ABC的进化卷积神经网络

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318301

Wenbo Zhu, Weichang Yeh, Jianwen Chen, Dafeng Chen, Aiyuan Li, Yangyang Lin

引用次数: 22

Research on Optimization of CAPTCHA Recognition Algorithm Based on SVM 基于SVM的CAPTCHA识别算法优化研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318355

Li Wei, Xiang Li, Tingrong Cao, Quan Zhang, LiangQi Zhou, Wenli Wang

引用次数: 3

Evaluating Acoustic Feature Maps in 2D-CNN for Speaker Identification 基于2D-CNN声学特征映射的说话人识别

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318386

Ali Shariq Imran, Vetle Haflan, Abdolreza Sabzi Shahrebabaki, Negar Olfati, T. Svendsen

{"title":"Evaluating Acoustic Feature Maps in 2D-CNN for Speaker Identification","authors":"Ali Shariq Imran, Vetle Haflan, Abdolreza Sabzi Shahrebabaki, Negar Olfati, T. Svendsen","doi":"10.1145/3318299.3318386","DOIUrl":"https://doi.org/10.1145/3318299.3318386","url":null,"abstract":"This paper presents a study evaluating different acoustic feature map representations in two-dimensional convolutional neural networks (2D-CNN) on the speech dataset for various speech-related activities. Specifically, the task involves identifying useful 2D-CNN input feature maps for enhancing speaker identification with an ultimate goal to improve speaker authentication and enabling voice as a biometric feature. Voice in contrast to fingerprints and image-based biometrics is a natural choice for hands-free communication systems where touch interfaces are inconvenient or dangerous to use. Effective input feature map representation may help CNN exploit intrinsic voice features that not only can address the instability issues of voice as an identifier for textindependent speaker authentication while preserving privacy but can also assist in developing efficacious voice-enabled interfaces. Three different acoustic features with three possible feature map representations are evaluated in this study. Results obtained on three speech corpora shows that an interpolated baseline spectrogram performs best compared to Mel frequency spectral coefficients (MFSC) and Mel frequency cepstral coefficient (MFCC) when tested on a 5-fold cross-validation method using 2D-CNN. On both textdependent and text-independent datasets, raw spectrogram accuracy is 4% better than the traditional acoustic features.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115892784","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: 0

A Deep Attention Network for Chinese Word Segment 汉语分词的深度注意网络

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318351

Lanxin Li, Ping Gong, L. Ji

引用次数: 1

A Robust Mature Tomato Detection in Greenhouse Scenes Using Machine Learning and Color Analysis 基于机器学习和颜色分析的温室场景成熟番茄鲁棒检测

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318338

Guoxu Liu, Shuyi Mao, Hui Jin, J. H. Kim

引用次数: 7

Multi-Path and Multi-Loss Network for Person Re-Identification 基于多路径多损失的人员再识别网络

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318331

Jiabao Wang, Shanshan Jiao, Yang Li, Zhuang Miao

引用次数: 0

An Exact Penalty Method for Top-k Multiclass Classification Based on L0 Norm Minimization 基于L0范数最小化的Top-k多类分类的精确惩罚方法

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318335

Haoxian Tan

引用次数: 5

Detecting Climate Change Deniers on Twitter Using a Deep Neural Network 使用深度神经网络检测推特上的气候变化否认者

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318382

Xingyu Chen, L. Zou, Bo Zhao

{"title":"Detecting Climate Change Deniers on Twitter Using a Deep Neural Network","authors":"Xingyu Chen, L. Zou, Bo Zhao","doi":"10.1145/3318299.3318382","DOIUrl":"https://doi.org/10.1145/3318299.3318382","url":null,"abstract":"Climate change or global warming is a global threat to both human communities and natural systems. In recent years, there is an increasingly public debate on the existence of climate change or global warming, but data describing such discussions are difficult to access. Social media provide a new data source to survey public perceptions and attitudes toward such topics. However, enabling computers to automatically determine users' attitudes towards climate change based on social media contents is still challenging. Taking Twitter data as an example, this study analyzed public discussions about climate change and global warming in year 2016. The objectives are: (1) to develop an optimized Deep Neural Network (DNN) classifier to identify users who are climate change deniers based on tweet contents; (2) to examine the temporal patterns of climate change discussions on Twitter and its driving factors. Results demonstrate that the developed DNN model successfully identified climate change deniers based on tweet contents with an overall accuracy of 88%. There are more climate change discussions during September to December 2016, whereas the percentages of climate change deniers were lower in the same period. Public interests and attitudes on climate change were driven by extreme weather events and environmental policy changes. The developed methodology will shed lights on the utility of deep learning in natural language processing, while the results provide improved understanding of factors affecting public attitudes on climate change.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125770304","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: 17

Application of ConvLSTM Network in Numerical Temperature Prediction Interpretation ConvLSTM网络在数值温度预报解释中的应用

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318381

Hong Lin, Yunzi Hua, Leiming Ma, Lei Chen

{"title":"Application of ConvLSTM Network in Numerical Temperature Prediction Interpretation","authors":"Hong Lin, Yunzi Hua, Leiming Ma, Lei Chen","doi":"10.1145/3318299.3318381","DOIUrl":"https://doi.org/10.1145/3318299.3318381","url":null,"abstract":"The application research of machine learning methods has attracted attention in the meteorological field. In this paper, we establish a spatiotemporal temperature deviation prediction model (PredTemp) based on convolution and long short-term memory network (ConvLSTM). The model is trained with numerical weatherprediction (NWP), and the prediction results are used to correct the temperature prediction in NWP. Exploring the influence of the weather elements added to the model on the prediction results is also the focus of this paper. Two datasets are constructed for this purpose: the temperature forecast deviationdataset (dataset1) is constructed by using the temperature forecastsand the analysis field in NWP, a precipitation forecast dataset (dataset2) was constructed using the precipitation forecasts in NWP. The experimental results show that the model is effective. Using dataset1 as dataset for training, the accuracy rate of temperaturecorrected by PredTempwas increased by 3%compared to NWP; using dataset1 and dataset2 as dataset for training, the accuracy rate of temperature corrected by PredTemp was increased by 4%. The addition of precipitation elements has played a positive role in improving the accuracy of the model prediction.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353159","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: 9

Minimizing the Misclassification Rate of the Nearest Neighbor Rule Using a Two-stage Method 用两阶段法最小化最近邻规则的误分类率

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318339

Yunlong Gao, Si-Zhe Luo, Jinyan Pan, Baihua Chen, Peng Gao

引用次数: 0