the proceedings contain 76 papers. the topics discussed include: a method for Chinese sarcasm detection based on enhanced cross-entropy and regularization;CNN-BiGRU-attention: a time series-based traffic flow predicti...
ISBN:
(纸本)9798350355925
the proceedings contain 76 papers. the topics discussed include: a method for Chinese sarcasm detection based on enhanced cross-entropy and regularization;CNN-BiGRU-attention: a time series-based traffic flow prediction model;inevitable exposure: analyzing the privacy paradox in the age of digital connectivity withmachinelearning paradigms;research on data fusion algorithms for non-stop overload detection on highways;deep learning based automatic detection algorithm of atrial fibrillation implemented on FPGA;the effect of data transformation techniques on machinelearning performance: a case study on student dropout prediction;a novel semi-supervised learning method using causal margin adaptation for imbalanced classification;and revolutionizing requirements elicitation: deep learning-based classification of functional and non-functional requirements.
Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important...
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ISBN:
(纸本)9798400703713
Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machinelearning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machinelearning techniques and properly deploy them for real-world applications in a scalable way. thus, we organize the 5thinternational Workshop on machinelearning on Graphs (MLoG)(1), held in conjunction withthe 17th ACM conference on Web Search and datamining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machinelearning on graphs.
Dealing withdata and model heterogeneity is crucial to federated learning practices. In this work, we introduce a novel mechanism termed SIO, which asks clients to take turn to be the server for aggregating model par...
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this study investigates the effects of data transformation techniques on the performance of various machinelearning classification models. Specifically, the standard scaler (or Z-score) and min-max scaler techniques ...
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Convolutional neural networks (CNNs) have achieved considerable success across a spectrum of computer vision tasks, with applications ranging from healthcare to automated driving. Recent literature has also explored i...
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ISBN:
(纸本)9798400710810
Convolutional neural networks (CNNs) have achieved considerable success across a spectrum of computer vision tasks, with applications ranging from healthcare to automated driving. Recent literature has also explored its potential utility in trading and risk management within the finance industry. Despite their versatility, CNNs are substantially constrained by their data-hungry nature. the lack of well-labeled image datasets poses a major challenge to the widespread adoption of CNNs in financial machinelearning research across academia and industry. To address these concerns, this work presents Generative-CNN, a novel approach that utilizes a generative adversarial network (GAN) to synthetically generate images to enhance the performance of a CNN with applications in trading.
the proceedings contain 69 papers. the special focus in this conference is on machinelearning and datamining in patternrecognition. the topics include: An efficient approximate EMST algorithm for color image segmen...
ISBN:
(纸本)9783319961323
the proceedings contain 69 papers. the special focus in this conference is on machinelearning and datamining in patternrecognition. the topics include: An efficient approximate EMST algorithm for color image segmentation;personalized blended E-learning system using knowledge base approach based on information processing speed cognitive;a tag2Vec approach for questions tag suggestion on community question answering sites;a crowd sensing approach to video classification of traffic accident hotspots;spam review detection using ensemble machinelearning;automatic keyphrase extraction using recurrent neural networks;the wild bootstrap resampling in regression imputation algorithm with a gaussian mixture model;association rule mining in fuzzy political donor communities;educational datamining: An application of regressors in predicting school dropout;memory efficient frequent itemset mining;reinforcement learning for computer vision and robot navigation;a fast two-level approximate euclidean minimum spanning tree algorithm for high-dimensional data;rule induction partitioning estimator: A new deterministic algorithm for data dependent partitioning estimate;A CNN based transfer learning model for automatic activity recognition from accelerometer sensors;a mixture of personalized experts for human affect estimation;learning to rank and discover for E-commerce search;a hybrid neural machine translation technique for translating low resource languages;prediction of re-tweeting activities in social networks based on event popularity and user connectivity;flow prediction versus flow simulation using machinelearning algorithms;A method of biomedical knowledge discovery by literature mining based on SPO predications: A case study of induced pluripotent stem cells;fuzzy networks model, a reliable adoption in corporations.
the proceedings contain 69 papers. the special focus in this conference is on machinelearning and datamining in patternrecognition. the topics include: An efficient approximate EMST algorithm for color image segmen...
ISBN:
(纸本)9783319961354
the proceedings contain 69 papers. the special focus in this conference is on machinelearning and datamining in patternrecognition. the topics include: An efficient approximate EMST algorithm for color image segmentation;personalized blended E-learning system using knowledge base approach based on information processing speed cognitive;a tag2Vec approach for questions tag suggestion on community question answering sites;a crowd sensing approach to video classification of traffic accident hotspots;spam review detection using ensemble machinelearning;automatic keyphrase extraction using recurrent neural networks;the wild bootstrap resampling in regression imputation algorithm with a gaussian mixture model;association rule mining in fuzzy political donor communities;educational datamining: An application of regressors in predicting school dropout;memory efficient frequent itemset mining;reinforcement learning for computer vision and robot navigation;a fast two-level approximate euclidean minimum spanning tree algorithm for high-dimensional data;rule induction partitioning estimator: A new deterministic algorithm for data dependent partitioning estimate;A CNN based transfer learning model for automatic activity recognition from accelerometer sensors;a mixture of personalized experts for human affect estimation;learning to rank and discover for E-commerce search;a hybrid neural machine translation technique for translating low resource languages;prediction of re-tweeting activities in social networks based on event popularity and user connectivity;flow prediction versus flow simulation using machinelearning algorithms;A method of biomedical knowledge discovery by literature mining based on SPO predications: A case study of induced pluripotent stem cells;fuzzy networks model, a reliable adoption in corporations.
Clustering is widely used technique to find hidden, meaningful patterns in the given dataset, called as clusters. It is grouping of data into groups of similar objects. the objective is that the objects within a group...
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this paper proposes a frequent patterndatamining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent patternmining algorithms in high-dimensional and ...
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