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检索条件"任意字段=4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023"

共 108 条记录，以下是11-20 订阅

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conference Committees

Conference Committees

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pattern recognition and machine learning (prml), ieee international conference on

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prml 2023 TOC

PRML 2023 TOC

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pattern recognition and machine learning (prml), ieee international conference on

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Comparison of two modern pattern recognition methods

Comparison of two modern pattern recognition methods

引用

4th international conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Shi Xiaochun Dalian Jiaotong Univ Dept Machine Engn Dalian Liaoning Prov Peoples R China

ISBN: (纸本)9780769532783

Two methods of pattern recognition are introduced in this paper: Unsupervised learning algorithm-fuzzy clustering method and supervised learning algorithm -neural network. the pattern recognition becomes failure pattern recognition if it is used in the fault diagnosis of the machine. Both merits and shortages of these two methods are discussed through a specific example in the mechanical faults diagnosis.

关键词： pattern recognition fuzzy clustering method neural network

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Data Augmentation Based on 3D Model Data for machine learning 4

Data Augmentation Based on 3D Model Data for Machine Learnin...

引用

4th ieee international conference on Computer and Communication Systems (ICCCS)

作者： Iwasaki, Masumi Yoshioka, Rentaro Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan

ISBN: (纸本)9781728113227

A new method of data augmentation for machine learning using 3D model data is proposed. the method involves the use of STL data of an object to automatically generate a set of training data covering a continuous range of view angles and various backgrounds. It also involves the use of two CNN's, one corresponding to the 'object (parent class)' and another to the 'view angle (child class)', that provides a two-stage classification to improve tolerance against over-classification. the performance of the method is demonstrated by comparing categorization results with conventional approach based on real-world photographs. the method shows satisfactory improvements over conventional method using photographed images.

关键词： component machine-learning pattern recognition image-processing

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Text-to-CQL Based on Large Language Model and Graph pattern Enhancement 5

Text-to-CQL Based on Large Language Model and Graph Pattern ...

引用

5th ieee international conference on pattern recognition and machine learning, prml 2024

作者： Zhao, Xinke Yilahun, Hankiz Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

With the wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query language. However, when performing graph database retrieval, the complex pattern and syntax make constructing CQL statements a complicated and time-consuming task. therefore, similar to Text-to-SQL, it is necessary and urgent to study an effective method for end-to-end transformation of natural languages into CQL. there have been many advances in Text-to-SQL for traditional relational databases, but these methods cannot be well applied to Text-to-CQL, so there is still a lack of effective work on Text-to-CQL for graph databases. In recent years, as large language models have been widely applied to different tasks with good results, and considering that the fundamental difference between CQL and SQL is the representation of graph patterns. We propose in this paper PA-LLM, a Text-to-CQL method that utilizes large language models combined with graph patterns enhancement, which combines large language models and subdivides the graph patterns into three categories according to their respective characteristics. they are simple query patterns, multi-hop query patterns, and function query patterns. For different graph patterns, the method optimizes the process of generating CQL for the model, which can be subdivided into four sub-methods, simple pattern enhancement method, multi-hop pattern enhancement method, function pattern enhancement method, and entity-relationship enhancement method. the experimental results show that the method improves the quality of the CQL statements generated by the model, including the logical accuracy ACCLX and the execution result accuracy ACCEX, and achieves better results on the SpCQL dataset proposed by the National Defense University of Science and Technology (NDUST). It provides an effective solution for the task of converting CQL t

关键词： Knowledge graph

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New Graph Embedding Approach for 3D Protein Shape Classification 9

New Graph Embedding Approach for 3D Protein Shape Classifica...

引用

Joint 9th international conference on Informatics, Electronics and Vision (ICIEV) / 4th international conference on Imaging, Vision and pattern recognition (ICIVPR)

作者： Madi, Kamel Paquet, Eric Umanis Res & Innovat F-92300 Levallois Perret France Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9781728193311

We address the problem of 3D protein deformable shape classification. Proteins are macromolecules characterized by deformable and complex shapes which are related to their function making their classification an important task. their molecular surface is represented by graphs such as triangular tessellations or meshes. In this paper, we propose a new graph embedding based approach for the classification of these 3D deformable objects. Our technique is based on graphs decomposition into a set of substructures, using triangle-stars, which are subsequently matched with the Hungarian algorithm. the proposed approach is based on an approximation of the Graph Edit Distance which is characterized by its robustness against both noise and distortion. Our algorithm defines a metric space using graph embedding techniques, where each object is represented by a set of selected 3D prototypes. We propose new approaches for prototypes selection and features reduction. the classification is performed with supervised machine learning techniques. the proposed method is evaluated against 3D protein benchmark repositories and state-of-the-art algorithms. Our experimental results consistently demonstrate the effectiveness of our approach. Contributions-We propose a new graph embedding approach to classify 3D deformable protein shapes and new techniques for prototypes selection and dimensionality reduction then we performed the classification using a Naive Bayes (NB) classifier and we achieve better results than the state-of-the-art.

关键词： 3D Protein Shape Classification 3D object recognition 3D Deformable object recognition pattern recognition Graph Embedding Graph matching Graph edit distance Graph decomposition Graph metric Metric learning

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Rumor Detection on Twitter using Extracted patterns from Conversational Tree 4

Rumor Detection on Twitter using Extracted Patterns from Con...

引用

4th international conference on Web Research (ICWR)

作者： Yavary, Arefeh Sajedi, Hedieh Univ Tehran Coll Sci Sch Math Stat & Comp Sci Dept Comp Sci Tehran Iran

ISBN: (纸本)9781538653647

these days, Twitter social network is one of the main platforms for getting news, among people over the world. this is because of the high volume of data generated by this social media, which makes Twitter up to date with news and information. Nevertheless, the existence of invalid information over the social network makes the users unhappy and also arises some problems in the real world, particularly in the crisis. To overcome these problems and other possible issues, automatic detection of rumor on Twitter must be taken into account. Despite such issues, in this paper, rumor detection in Twitter is studied. In this paper, the rumor is validated by considering the user's feedback, as the source data for rumor study. In our proposed method, pattern recognition and its analysis of the user conversational tree in Twitter is studied. these recognized patterns feed into as features for training a classifier for rumor detection. the model for training a classifier is an Extreme learning machine and its extension. the dataset for experiments of our method is the standard dataset of SemEval-2017 Task 8. Experiments of our proposed method with respect to competitor methods in rumor detection show that our method outperforms the state of the art methods.

关键词： Rumor Detection Twitter Social Network Conversational Tree pattern recognition Extreme learning machine

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Fuzzy recognition of graphite morphology in nodular cast iron based on evolution strategy

Fuzzy recognition of graphite morphology in nodular cast iro...

引用

4th international conference on machine learning and Cybernetics

作者： Chen, ZB Yu, YQ Chen, HQ Yang, SM Guangdong Univ Technol Fac Comp Guangzhou 510090 Peoples R China

ISBN: (纸本)0780390911

the Morphology of graphite is a decisive factor that affects the performance of the nodular cast iron. In this paper, the basic types of graphite morphology in the nodular cast iron are introduced. the morphological features used in the recognition are defined. During the establishment of fuzzy recognition system, we apply the evolution strategy to computing weight coefficient of every feature. the results of experiment show that the method can effectively recognize the graphite with types of morphology in the nodular cast iron.

关键词： fuzzy system evolution strategy pattern recognition graphite morphology nodular cast iron

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A bayesian framework for regularized SVM parameter estimation

A bayesian framework for regularized SVM parameter estimatio...

引用

4th ieee international conference on Data Mining

作者： Gregor, J Liu, ZQ Univ Tennessee Dept Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)0769521428

the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of,the learning samples that we call the support set and then determines not only the weights of the classifier but, also the hyperparameter that controls the influence of the regularizing penalty term, on basis thereof. We provide numerical results using several data sets from the public domain.

关键词： pattern recognition

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Employing machine learning algorithms to discover risk factors of glaucoma 4

Employing machine learning algorithms to discover risk facto...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Al-Shamiri, Abdulkawi Yahya Radman School of Computer Science and Engineering Hodeidah University Department of Computer Science Al-Hodaidah Yemen

ISBN: (纸本)9781665413220

the statistics of the World Health Organization indicated that blindness or loss of vision in one of the eyes is one of the many threats and challenges in the world due to the increasing number of people infected annually. One of the many serious eye diseases is glaucoma, which leads to the loss of vision in the affected eye. Most studies on glaucoma are based on clinical trials and thus lack insights into the risk factors of glaucoma;these factors could be obtained from larger number of doctors and patients and analyzed sufficiently. With the recent development of technology, the need of utilizing machine learning algorithms in the medical field has increased to achieve a better and accurate health care system. thus, this study aims to effectively discover the hidden risk factors of glaucoma by machine learning algorithms using real data collected from doctors and patients. the author used one of decision tree algorithms so-called J48 and one of association rule algorithm socalled apriori to analyze the collected data. the study shows that some of the risk factors of glaucoma which have been proven by medical trials are also obtained by means of the two algorithms. therefore, machine learning algorithms can be used to effectively determine causes of any disease. the results not only provide a novel method to discover the risk factors of glaucoma but also reveal new risk factors of glaucoma which have not been discovered previously by studies based on medical trials such as discovery the relation chronic constipation and stomach upset with glaucoma as reasons leading to glaucoma. this study will contribute to avoiding blindness and preventing it by determining the recognition factors of glaucoma. © 2021 ieee.

关键词： pattern recognition

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