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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是201-210 订阅

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A Network Intrusion Detection Method for Training dataset Generation Based on KNN 24

A Network Intrusion Detection Method for Training Dataset Ge...

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Liu, Xiaoan Shuang, Nian Saklain, Md Golam Huang, Hui Zhang, Liqing Deng, Fei Kunming Univ Coll Informat Engn Kunming Yunnan Peoples R China Univ Yunnan Lab Data Governance & Intelligent Decis Kunming Yunnan Peoples R China

ISBN: (纸本)9798400707032

In this paper, for overcoming the difficulty of training dataset about two random method and manual method, the K-Nearest Neighbor (KNN) method is used to detect the network intrusion. Through adjusting K-value from part dataset and overall dataset, it can be calculated that the accuracy rate of random method is higher than of manual method for network intrusion detection by KNN. Finally, based on CICIDS2017 dataset, the experiments can verity the effectiveness of the proposed method.

关键词： KNN Training dataset Random method Manual method Network intrusion

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1st international workshop on pattern recognition with Support Vector machines, SVM 2002

1st International Workshop on Pattern Recognition with Suppo...

引用

1st international workshop on pattern recognition with Support Vector machines, SVM 2002

ISBN: (纸本)354044016X

The proceedings contain 32 papers. The special focus in this conference is on Computational Issues, Object recognition, pattern recognition and Applications. The topics include: Predicting signal peptides with support vector machines;scaling large learning problems with hard parallel mixtures;on the generalization of kernel machines;kernel whitening for one-class classification;support vector machines with embedded reject option;combining color and shape information for appearance-based object recognition using ultrametric spin glass-Markov random fields;maintenance training of electric power facilities using object recognition by SVM;advances in component-based face detection;support vector learning for gender classification using audio and visual cues;analysis of nonstationary time series using support vector machines;anomaly detection enhanced classification in computer intrusion detection;sparse correlation kernel analysis and evolutionary algorithm-based modeling of the sensory activity within the rat’s barrel cortex;applications of support vector machines for pattern recognition;typhoon analysis and data mining with kernel methods;support vector features and the role of dimensionality in face authentication;face detection based on cost-sensitive support vector machines;real-time pedestrian detection using support vector machines;a hybrid hmm/SVM approach to sequence recognition;an application to license plate localization;support vector machines in relational databases;multi-class SVM classifier based on pairwise coupling;face recognition using component-based SVM classification and morphable models;optimization of the SVM kernels using an empirical error minimization scheme and a comparative study of polynomial kernel SVM applied to appearance-based object recognition.

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Enhancing Unsupervised Few-Shot Medical Image Classification with Weight-Enhanced Contrastive learning 24

Enhancing Unsupervised Few-Shot Medical Image Classification...

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Liu, Huantong Zhong, Jingze Shanghai Univ Shanghai Peoples R China

ISBN: (纸本)9798400707032

In the realm of contemporary deep learning, the pivotal challenges confronting few-shot and unsupervised learning reside in harnessing scarce labeled samples alongside abundant unlabeled ones, particularly in the context of medical image classification. This paper introduces an innovative approach that seamlessly integrates dynamic clustering with weights augmentation, aimed at bolstering the performance of few-shot medical image classification. Dubbed Weight-Enhanced Contrastive learning (WECL), our method ingeniously fuses contrastive representation learning with a dynamic memory module during unsupervised pre-training. This fusion facilitates efficient clustering and classification of diverse augmented renditions of the same image. Additionally, the weights augmentation tactic meticulously tunes the weights of both ResNet and teacher-student model branches, thereby mitigating sample bias and enhancing the pre-trained model's proficiency. Extensive experiments across multiple few-shot medical image classification datasets underscore the superiority of our WECL approach, outperforming current state-of-the-art baselines, and effectively addressing issues pertaining to data distribution disparities and sample scarcity.

关键词： Medical Image Classification Weights Augmentation Contrastive learning Unsupervised learning Few-Shot learning

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The Neighborhood MCMC sampler for learning Bayesian networks 1

The Neighborhood MCMC sampler for learning Bayesian networks

引用

1st international workshop on pattern recognition (IWPR)

作者： Alyami, Salem A. Azad, A. K. M. Keith, Jonathan M. Monash Univ Sch Math Sci Clayton Vic 3800 Australia Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Riyadh Saudi Arabia

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f (N, E\D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

关键词： Directed acyclic graph structure inference local maxima graph space

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data mining Application in Smart Meter Quality Control

Data Mining Application in Smart Meter Quality Control

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international Conference on Fuzzy System and data mining (FSDM)

作者： Zhu, Yu-Nan Ji, Feng Liu, Jian Tian, Zheng-Qi Zhou, Chao Mahjoubfar, Ata Elect Power Metering Key Lab State Grid 9 Aoti St Nanjing Jiangsu Peoples R China State Grid Jiangsu Elect Power Res Inst 9 Aoti St Nanjing Jiangsu Peoples R China Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA Calif NanoSyst Inst Los Angeles CA USA

ISBN: (纸本)9781614996194;9781614996187

This paper presents application of data mining in enhancement of smart meter quality control based on the observed life cycle data. data mining techniques followed by machine learning methods efficaciously recognize intricate smart meter usage and failure patterns, generating unique insights, which lead to solutions for better quality control results.

关键词： data mining quality control smart meter pattern recognition

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Research on High Reliability 1553B Bus RT Mode data Receiving Method 24

Research on High Reliability 1553B Bus RT Mode Data Receivin...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Zhou, Li Wang, Yunhui Xue, Yijuan Jiang, Biao Xue, Jiao Elect Mech Res Inst Xian Aerosp Precis Xian Shaanxi Peoples R China

ISBN: (纸本)9798400707032

As the core control communication bus in the field of aerospace and weapons, 1553B bus has the characteristics of high reliability, high real-time, reasonable error control measures, high bus efficiency, and is widely used in rocket, satellite, spacecraft and other space fields, as well as modern aircraft avionics system. RT is a slave device of the 1553B bus, subject to the command of the bus controller, responsible for ready to receive and send data, and operate according to the data command of the bus controller, in order to ensure the reliability and efficiency of the 1553B bus system. In RT mode, how to receive bus controller commands correctly, efficiently and reliably and ensure the correct execution of commands has become a crucial part of RT system work. This paper presents three kinds of data receiving software implementation methods under RT mode of 1553B bus, which can realize real-time, accuracy and reliability of software data receiving.

关键词： 1553B software design RT data reception High reliability

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Acquisition of Typical Occupancy Schedules for Commercial Buildings from Social Networks 1

Acquisition of Typical Occupancy Schedules for Commercial Bu...

引用

1st ACM international workshop on Urban Building Energy Sensing, Controls, Big data Analysis, And Visualization (UrbSys)

作者： Lu, Xing Feng, Fan O'Neill, Zheng Univ Alabama Dept Mech Engn Tuscaloosa AL 35487 USA

ISBN: (纸本)9781450370141

Occupancy behavior in buildings has been a hotspot of research as building systems become more sophisticated. Traditional sensing technologies suffer from high costs, inevitable sensor errors, and scalability issues and thus are not widely implemented in buildings. In this notes paper, two different approaches for extracting the typical occupancy schedules for the input to the building energy simulation are explored based on the data from social networks. The first approach is to use text classification algorithms to identify whether people are present in space where they are making posts on social networks. To achieve this, word embedding and machine learning algorithms for the classification are used. On top of that, we could extract the typical occupancy schedules by assuming certain people counting rules. The second approach is to utilize the processed GPS location tracking data provided by social network giants such as Facebook and Google Map. Web scraping techniques are used to obtain the data and extract the building typical occupancy schedules. Two preliminary case studies demonstrate these two approaches as a proof of concept using a museum building, the Art Institute of Chicago. The results show that the extracted building occupancy schedules from different social network data sources (Twitter, Facebook, and Google Map) share a similar trend but slightly distinct with each other, which requires more explorations and further validations and corrections. However, the methodology and promising results from this preliminary study will lay the foundation of the occupancy sensing through the social network data mining, which aims to provide another data source for occupancy sensing in buildings at the building level. This will provide another alternative to estimate the occupancy at the community- and urban-scale.

关键词： Building Occupancy data Acquisition machine learning machine learning

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Evaluating the Max-Min Hill-Climbing Estimation of Distribution Algorithm on B-Functions 6th

Evaluating the Max-Min Hill-Climbing Estimation of Distribut...

引用

6th international workshop on Artificial Intelligence and pattern recognition (IWAIPR)

作者： Madera, Julio Ochoa, Alberto Univ Camaguey Dept Comp Sci Camaguey Cuba Inst Cibernet Matemat & Fis Havana Cuba

ISBN: (纸本)9783030011321;9783030011314

In this paper we evaluate a new Estimation of Distribution Algorithm (EDA) constructed on top of a very successful Bayesian network learning procedure, Max-Min Hill-Climbing (MMHC). The aim of this paper is to check whether the excellent properties reported for this algorithm in machine learning papers, have some impact on the efficiency and efficacy of EDA based optimization. Our experiments show that the proposed algorithm outperform wellknown state of the art EDA like BOA and EBNA in a test bed based on B-functions. On the basis of these results we conclude that the proposed scheme is a promising candidate for challenging real-world applications, specifically, problems related to the areas of data mining, Patter recognition and Artificial Intelligence.

关键词： Estimation of distribution algorithms B-functions Bayesian networks Dependency learning Evolutionary optimization

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Proceedings of the 1st ACM SIGSPATIAL international workshop on Advances in Resilient and Intelligent Cities, ARIC 2018

Proceedings of the 1st ACM SIGSPATIAL International Workshop...

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1st ACM SIGSPATIAL international workshop on Advances in Resilient and Intelligent Cities, ARIC 2018

ISBN: (纸本)9781450360395

The proceedings contain 8 papers. The topics discussed include: multiple evaluation in future population distribution for sustainable city;detecting street signs in cities based on object recognition with machine learning and GIS spatial analysis;D-record: disaster response and relief coordination pipeline;analysis, integration and visualization of urban data from multiple heterogeneous sources;SCOUTS: a smart community centric urban heat monitoring framework;real-time traffic light detection from videos with inertial sensor fusion;and an integrated visual analytics framework for spatiotemporal data.

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Research on a Software System Size Evaluation Model Combining BiLstM-CRF with Domain Adaptation learning 24

Research on a Software System Size Evaluation Model Combinin...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Sun, Sen Tao, Yizheng China Acad Engn Phys Inst Comp Applicat Beijing Peoples R China

ISBN: (纸本)9798400707032

In the field of software engineering, accurate and efficient measurement of system size is crucial for project management and decision-making. Traditional software system size evaluation methods, such as expert decision-making methods, COCOMO models, function point methods, and analogical methods, have problems such as relying on manual labor, low efficiency, and susceptibility to subjective factors. To address these issues, this paper proposes a Dalse (Domain Adaptive learning System Evaluation) automated method for evaluating software function size. Based on the IFPUG function point standard, this method constructs a BiLstM-CRF model for identifying function types, and introduces domain adaptive learning technology to solve the problem of insufficient labeled text data for domain-specific annotation. This enables the method to flexibly adapt to the characteristics of software in specific domains, achieving automated extraction and quantitative calculation of software function points. Experimental validation shows that the method's precision, recall, and F1 score reached 83%, 80%, and 82%, respectively, which is a 5% improvement compared to the accuracy of pure BiLstM-CRF function point recognition.

关键词： Software Scale Evaluation BiLstM-CRF Domain Adaptation learning

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