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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是1-10 订阅

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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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Graph based Clustering Techniques 15

Graph based Clustering Techniques

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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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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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Optimisation of machine learning Based data mining Methods for Network Intrusion Detection 24

Optimisation of Machine Learning Based Data Mining Methods f...

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6th international conference on Big-data Service and Intelligent Computation (BDSIC)

作者： Li, Mingxiao Li, Ziqing Liu, Chenlong Chen, Wanqi Ma, Chaojie Qinghai Minzu Univ Xining Peoples R China

ISBN: (纸本)9798400718069

In the context of today's big data era, traditional data mining techniques often rely on manual feature extraction, which limits their accuracy and generalization capabilities. Additionally, handling large-scale data poses challenges, especially in network intrusion detection, where traditional methods may struggle to effectively process and analyze massive and complex network traffic data, thereby failing to accurately detect and prevent potential intrusions. In contrast, machine learning-based data mining methods offer better generalization and the ability to handle large-scale data, automatically learning complex patterns and relationships within extensive network traffic, and providing better interpretability. Against this backdrop, this paper employs machine learning methods to deeply mine network intrusion traffic data, encompassing data cleaning, imbalanced data handling, and feature selection. the study optimizes the modules for handling imbalanced data and feature selection, and compares different classification models to select the best model for network intrusion detection. To validate the effectiveness of this approach, evaluations are conducted using confusion matrices and a series of metrics (accuracy, precision, recall, F1-score, and AUC), along with comparisons to previous research results. the results not only confirm the effectiveness of the proposed method but also offer a reliable solution to cope with the continuously growing volume of network traffic data.

关键词： data mining Imbalance data processing Feature selection Network intrusion detection

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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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the Application of Logistic Regression in Frequency Domain Classification Based on Deep learning 6

The Application of Logistic Regression in Frequency Domain C...

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6th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2024

作者： Liu, Xiaojia Zhang, Tianbiao Wuhan Donghu University Wuhan China Hunan Post and Telecommunication College Hunan China

ISBN: (纸本)9798331541798

the paper bases on the theory of deep learning, uses the Scikit-learn machine learning framework and logistic regression algorithm, combines with supervised machine learning. through Fourier transform, the time-domain signal is transformed into the frequency domain for research. For different types of music, the regression discrete values of logical values are determined, and finally testing result of recognition accuracy of over 90%. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-time Gesture recognition for Sign Language Using machine learning 6

Real-time Gesture Recognition for Sign Language Using Machin...

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6th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

作者： Uke, Shailaja Bari, Rutika Bapat, Atharv Barde, Keyur Bhalke, Shantanu Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798331505790

A Real time gesture recognition system employs and leverages on the concepts of machine learning to interpret and respond promptly to the user gestures, significantly enhancing the Human Computer Interaction. Instead of analyzing one frame and predicting the gesture, we use a set of frames to determine the action. Media Pipe is used for the process for key point extraction of the hand joints, which provides comprehensive data for the analysis. For the prediction in real time, the Long Short Term Memory (LSTM) model is trained using TensorFlow. Keras is used for building and training neural networks in Python, alongside TensorFlow to streamline the model development process. this system aims to have seamless interaction between the machine learning model and the gesture data extraction to enhance the user experience and opens up new possibilities for intuitive and natural communication with computers. © 2024 IEEE.

关键词： Gesture recognition

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Few-shot learning with Prompting Methods 6

Few-shot Learning with Prompting Methods

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6th international conference on pattern recognition and Image Analysis (IPRIA)

作者： Bahrami, Morteza Mansoorizadeh, Muharram Khotanlou, Hassan Bu-Ali Sina University Department of Computer engineering Hamedan Iran

ISBN: (纸本)9798350314557

Today, in natural language processing, labeled data is important, however, getting adequate amount of data is a challenging step. there are many tasks for which it is difficult to obtain the required training data. For example, in machine translation, we need to prepare a lot of data in the target language, so that the work performance is acceptable. We may not be able to collect useful data in the target language. Hence, we need to use few-shot learning. Recently, a method called prompting has evolved, in which text inputs are converted into text with a new structure using a certain format, which has a blank space. Given the prompted text, a pre-trained language model replaces the space with the best word. Prompting can help us in the field of few-shot learning;even in cases where there is no data, i.e. zero-shot learning. Recent works use large language models such as GPT-2 and GPT-3, with the prompting method, performed tasks such as machine translation. these efforts do not use any labeled training data. But these types of models with a massive number of parameters require powerful hardware. pattern-Exploiting Training (PET) and iterative pattern-Exploiting Training (iPET) were introduced, which perform fewshot learning using prompting and smaller pre-trained language models such as Bert and Roberta. For example, for the Yahoo text classification dataset, using iPET and Roberta and ten labeled datasets, 70% accuracy has been reached. this paper reviews research works in few-shot learning with a new paradigm in natural language processing, which we dub prompt-based learning or in short, prompting.

关键词： few-shot learning natural language processing prompting prompt-based learning pre-trained language model

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the Performance Analysis of Several machine learning Methods in Predicting Diabetes 6

The Performance Analysis of Several Machine Learning Methods...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wu, Yuanyuan Xia, Zhuomin Liu, Huizhou Huang, Mengxing Zhang, Yu Hainan University College of Information and Communication Engineering Haikou570228 China Hainan University College of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798350325485

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine's open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods. © 2023 IEEE.

关键词： Decision support systems

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