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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3325 条记录，以下是1-10 订阅

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2023 IEEE 4th international conference on pattern recognition and machine learning, PRML 2023

2023 IEEE 4th International Conference on Pattern Recognitio...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

ISBN: (纸本)9798350324303

the proceedings contain 98 papers. the topics discussed include: self-supervised learning for point clouds through multi-crop mutual prediction;assessment and prediction of corrosion rate of marine railway bridges based on ridge regression model;Chunk-BERT: boosted keyword extraction for long scientific literature via BERT with chunking capabilities;chunking of Setswana noun and verb phrase;RarKGQA: multi-hop question and answering method based on knowledge graph embedding;diagnosis and identification of citrus canker growth rate using machine learning;joint spatial similarity-based attention for retina vessel segmentation with super resolution encoding;mixed attention interleaved execution cascade architecture for fittings instance segmentation in overhead transmission line images;and RFBiCF: a relation-first bidirectional cascade framework for relational triple extraction.

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machine learning Techniques for pattern recognition in High-Dimensional data mining 4

Machine Learning Techniques for Pattern Recognition in High-...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Li, Pochun Northeastern University Boston United States

ISBN: (纸本)9798331534622

this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications. © 2024 IEEE.

关键词： Adversarial machine learning

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14th international conference on machine learning and data mining in pattern recognition, MLDM 2018

14th International Conference on Machine Learning and Data M...

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14th international conference on machine learning and data mining in pattern recognition, MLDM 2018

ISBN: (纸本)9783319961323

the proceedings contain 69 papers. the special focus in this conference is on machine learning and data mining in pattern recognition. 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 machine learning;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 data mining: 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 machine learning 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.

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14th international conference on machine learning and data mining in pattern recognition, MLDM 2018

14th International Conference on Machine Learning and Data M...

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14th international conference on machine learning and data mining in pattern recognition, MLDM 2018

ISBN: (纸本)9783319961354

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Research on Student Performance Prediction Based on Clustered Graph Neural Networks 4

Research on Student Performance Prediction Based on Clustere...

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4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Lai, Xiaochen Zhao, Sheng Zhang, Zheng Pan, Xiaodi Dalian Univ Technol Sch Software Dalian Peoples R China

ISBN: (纸本)9798350375084;9798350375077

the advent of the big data era has brought about profound changes in modern education, making educational data mining an important field within the realm of data analysis. Predicting students' academic performance is one of the crucial topics in this field. this paper proposes a student performance prediction method: Cluster-based Graph Neural Network Prediction (CGNNP). By clustering students' routine and final exam scores and using the clustering results as category labels, the method employs graph neural networks for training and testing the data. through analyzing students' background information and learning behavior data, this method effectively predicts students' performance. Experimental results demonstrate a significant improvement in prediction accuracy compared to traditional machine learning methods, better reflecting students' learning situations and providing educators with more accurate decision-making support.

关键词： component clustering graph neural networks educational data mining

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Perspective of deep learning strategies for analysis of 1D biomedical signals 4th

Perspective of deep learning strategies for analysis of 1D b...

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4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Vishwanath, K.R. Rohith, V. Vinutha, D.C. Vijay, C.P. Rao, Soundarya Govinda Sriraam, N. VTU India VVCE India

ISBN: (纸本)9781032426853

Artificial Intelligence and machine learning encompass a broader range of technologies, of which Deep learning is a specific subset and it is expeditiously evolving in the medical field. Most medical devices or imaging techniques use deep learning as a critical tool for analysing the data. the proposed study is a literature review on the methodologies that are used for the sleep -wake cycle detection. the importance of deep learning in the field of ECG, EEG and analysis of infant cry signals is also demonstrated in the sections below. Further analytical studies explain about the facial pattern recognition of the new-born in NICU and also discuss the benefits of analysing the data especially in the case of infants. © 2024 the Author(s).

关键词： Electrocardiograms

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Graph based Clustering Techniques 15

Graph based Clustering Techniques

引用

15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pawar, Vaishali Chandwadkar, Radhika MVPS's KBTCOE Computer Engineering Department Nashik India

ISBN: (纸本)9798331300579

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 should be similar to one another and different from the objects in other groups. Clustering plays an excellent role in data mining applications, biology, science, pattern recognition, social networking, machine learning, web analysis, CRM, marketing, medical diagnostics, and many others. A variety of clustering algorithms have emerged that meet these requirements and were successfully applied to many data mining and pattern recognition problems. Due to their great representational power, Graphs can handle huge databases with large attributes. Hence Graphs play a vital role in clustering applications. © Grenze Scientific Society, 2024.

关键词： Clustering data mining machine learning pattern recognition social networking

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Multi Dimensional Deep Encoding for Categorical Feature Space 24

Multi Dimensional Deep Encoding for Categorical Feature Spac...

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13th international conference on Computing and pattern recognition, ICCPR 2024

作者： Alamuri, Madhavi Surampudi, Bapiraju Negi, Atul School of Computer and Information Sciences University of Hyderabad Telangana State Hyderabad India Cognitive Science Lab International Institute of Information Technology Telangana State Hyderabad India

ISBN: (纸本)9798400717482

Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machine learning. It is crucial to understand how to provide categorical input to machine learning algorithms because the majority of them are developed for numerical inputs. Any machine learning algorithm's performance depends not only on the model and hyper parameters but also on the way the data is prepared for and fed into the model. With high cardinality features and categorical predictor variables, machine learning approaches that handle categorical data frequently encounter challenges. In this scenario, we investigate categorical encoding techniques, develop a taxonomy of encoding techniques, and use the machine learning algorithms KNN, MLP, SVM, and ELM and make a comparative study. We suggest a method of hybrid encoding using CNN that gives better performance results. In this paper, we propose the machine learning strategy employing multi-dimensional encodings with CNN, which outperforms other machine learning approaches on majority datasets. It is clear that CNN's performance in learning categorical features has been much enhanced by the use of multi-dimensional encoded input vectors. In our suggested method for supervised and unsupervised encoding, we have used four distinct encodings. these results encourage us to further study the outcomes of different hybrid combinations of encodings for categorical feature classification. © 2024 Copyright held by the owner/author(s).

关键词： Categorical data

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Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. this study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. the experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

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Tool Wear State recognition Based on Multi-source Feature Fusion and Deep learning 4

Tool Wear State Recognition Based on Multi-source Feature Fu...

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4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Song, Ning Yu, Yuna Han, Tongtongn Xie, Guihua Mo, Desheng Li, Na Qilu Inst Technol Jinan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Aiming at the problems of single feature dimension and low accuracy in condition monitoring only from vibration characteristics and tool wear characteristics. In this paper, a tool wear state recognition method based on multi-source feature fusion and deep learning is proposed. the main innovation of our work lies in two aspects: firstly, the tool wear data is processed by multi-source feature fusion method, which effectively fuses multiple sensor signals and improves the efficiency and accuracy of data processing. Secondly, we optimize GRU (Gated Recursive Unit) model by combining whale algorithm for parameter optimization and add XGBoost to improve the prediction performance and robustness. Different milling wear experimental data sets are used to verify the recognition performance of the training model. the experimental results show that compared with the traditional methods, our proposed method has obvious advantages in tool wear state recognition.

关键词： tool wear multi-source feature fusion deep learning GRU whale algorithm

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