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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441-450 订阅

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Conformal prediction for distribution-independent anomaly detection in streaming vessel data

Conformal prediction for distribution-independent anomaly de...

引用

1st international workshop on Novel data stream pattern mining Techniques, streamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and data mining

作者： Laxhammar, Rikard Falkman, Göran University of Skövde Skövde Sweden Security and Defense Solutions Saab AB Järfälla Sweden

ISBN: (纸本)9781450302265

This paper presents a novel application of the theory of conformal prediction for distribution-independent on-line learning and anomaly detection. We exploit the fact that confor-mal predictors give valid prediction sets at specified confidence levels under the relatively weak assumption that the (normal) training data together with (normal) observations to be predicted have been generated from the same distribution. If the actual observation is not included in the possibly empty prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the significance level as an upper bound of the probability that a normal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the rate of false alarms without having to find any application specific thresholds. The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. The validity of the prediction sets is justified by the empirical error rate which is just below the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity is superior to that of two previously proposed methods. © 2010 ACM.

关键词： Forecasting

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machine learning Models for the recognition of Commands in Smart Home Technologies 1

Machine Learning Models for the Recognition of Commands in S...

引用

1st international Conference on Smart Automation and Robotics for Future Industry, SMARTINDUstRY 2024

作者： Mochurad, Lesia Babii, Viktoriia Greguš, Michal Lviv Polytechnic National University 12 Bandera street Lviv79013 Ukraine Faculty of Management Comenius University Bratislava Odbojárov 10 Bratislava 25820 05 Slovakia

Researching the possibilities of using smart home technologies to support the independent life of lonely elderly people involves considering various aspects that can improve their comfortable and safe stay at home. For instance, health monitoring systems, access to medical information resources, nutritional care, home security, assistance in everyday affairs. The main purpose of this work is to research the application of data analysis methods and machine learning to develop a model that can recognize commands and perform various functions to ensure a comfortable and safe life for older people. machine learning models used to recognize smart home commands such as Random Forest (RF), Support vector method (SVM), XGBoost, CatBoost, Multilayer Perceptron (MLP), Lightgradient boosted machine (LGBM) was analyzed. The models that were trained used data collected from Kaggle, a public repository of datasets. Optimal parameters in machine learning algorithms for the classification of smart home commands were determined. As a result of the comparative analysis conducted according to the experimental assessment, the MLP deep learning model demonstrated better results compared to other methods. The proposed model achieved accuracy at 91%. This indicator can be improved in the future by using different deep learning models. © 2024 Copyright for this paper by its authors.

关键词： Deep learning

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Overview of the First workshop on Medical Content-Based Retrieval for Clinical Decision Support at MICCAI 2009

Overview of the First Workshop on Medical Content-Based Retr...

引用

1st MICCAI international workshop on Medical Content-Based Retrieval for Clinical Decision Support

作者： Mueller, Henning Kalpathy-Cramer, Jayashree Caputo, Barbara Syeda-Mahmood, Tanveer Wang, Fei Univ Hosp Geneva Geneva Switzerland Univ Geneva CH-1211 Geneva 4 Switzerland Univ Appl Sci Western Switzerland HES SO Sierre Switzerland Oregon Hlth & Sci Univ Portland OR 97201 USA IDIAP Res Ctr Martigny Switzerland IBM Corp Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9783642117688

In this paper, we provide an overview of the first workshop on Medical Content Based Retrieval for Clinical Decision Support (MCBR-CDS), which was held in conjunction with the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in 2009 in London, UK. The goal of the workshop was to bring together researchers from diverse communities including medical image analyses, text and image retrieval, data mining, and machine learning to discuss new techniques for multimodal image retrieval and the use of images in clinical decision support. We discuss the motivation for this workshop, provide details about the organization and participation, discuss the current state of the art in clinical image retrieval and the use of images for clinical decision support. We conclude with open issues and challenges that lie ahead for the domain of medical content based retrieval.

关键词： Content based retrieval

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mining stock market tendency using GA-based support vector machines

Mining stock market tendency using GA-based support vector m...

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1st international workshop on Internet and Network Economics, WINE 2005

作者： Yu, Lean Wang, Shouyang Lai, Kin Keung Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China School of Management Graduate University Chinese Academy of Sciences Beijing 100039 China College of Business Administration Hunan University Changsha 410082 China Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)3540309004

In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e.g., statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration. © Springer-Verlag Berlin Heidelberg 2005.

关键词： learning systems

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An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis 1

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

作者： Javed, Rabia Saba, Tanzila Humdullah, Salman Mohd Jamail, Nor Shahida Javed Awan, Mazhar Lahore College for Women University Computer Science Department Lahore54300 Pakistan Prince Sultan University Computer and Information Science Riyadh11586 Saudi Arabia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru81310 Malaysia University of Management and Technology Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9780738131771

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier. © 2021 IEEE.

关键词： Diagnosis

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1st international Conference on Soft Computing in data Science, SCDS 2015

1st International Conference on Soft Computing in Data Scien...

引用

1st international Conference on Soft Computing in data Science, SCDS 2015

ISBN: (纸本)9789812879356

The proceedings contain 25 papers. The special focus in this conference is on data mining and Fuzzy Computing. The topics include: An improved particle swarm optimization via velocity-based reinitialization for feature selection;classifying forum questions using PCA and machine learning for improving online CQA;data projection effects in frequent itemsets mining;data quality issues in data migration;reviewing classification approaches in sentiment analysis;possibility vague soft expert set theory and its application in decision making;an iterative method for solving fuzzy fractional differential equations;contrast comparison of flat electroencephalography image;an autocatalytic model of a pressurized water reactor in a nuclear power generation;selfish gene image segmentation algorithm;solving curriculum based course timetabling by hybridizing local search based method within harmony search algorithm;a parallel latent semantic indexing (LSI) algorithm for malay hadith translated document retrieval;short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine;implementation of dynamic traffic routing for traffic congestion;a comparative study of video coding standard performance via local area network;partial differential equation (PDE) based image smoothing system for digital radiographic image;main structure of handwritten jawi sub-word representation using numeric code;evaluating the usability of homestay websites in Malaysia using automated tools;humanoid-robot intervention for children with autism and accuracy assessment of urban growth pattern classification methods using confusion matrix and roc analysis.

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CALDS: Context-aware learning from data streams

CALDS: Context-aware learning from data streams

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1st international workshop on Novel data stream pattern mining Techniques, streamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and data mining

作者： Gomes, João Bártolo Menasalvas, Ernestina Sousa, Pedro A.C. Universidad Politécnica de Madrid Facultad de Informática Madrid Spain Universidade Nova de Lisboa Faculdade de Ciências e Tecnologia Lisboa Portugal

ISBN: (纸本)9781450302265

Drift detection methods in data streams can detect changes in incoming data so that learned models can be used to represent the underlying population. In many real-world scenarios context information is available and could be exploited to improve existing approaches, by detecting or even anticipating to recurring concepts in the underlying population. Several applications, among them health-care or recommender systems, lend themselves to use such information as data from sensors is available but is not being used. Nevertheless, new challenges arise when integrating context with drift detection methods. Modeling and comparing context information, representing the context-concepts history and storing previously learned concepts for reuse are some of the critical problems. In this work, we propose the Context-aware learning from data streams (CALDS) system to improve existing drift detection methods by exploiting available context information. Our enhancement is seamless: we use the association between context information and learned concepts to improve detection and adaptation to drift when concepts reappear. We present and discuss our preliminary experimental results with synthetic and real datasets. © 2010 ACM.

关键词： Population statistics

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FAM-based fuzzy inference for detecting shot transitions

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2nd international workshop on machine learning and data mining in pattern recognition, MLDM 2001

作者： Jang, Seok-Woo Kim, Gye-Young Choi, Hyung-Il Soongsil University 1-1 Sangdo-5 Dong Dong-Jak Ku Seoul Korea Republic of

ISBN: (纸本)3540423591

We describe a fuzzy inference approach for detecting and classifying shot transitions in video sequences. Our approach basically extends FAM(Fuzzy Associative Memory) to detect and classify shot transitions, including cuts, fades and dissolves. We consider a set of feature values that characterize differences between two consecutive frames as input fuzzy sets, and the types of shot transitions as output fuzzy sets. An initial implementation runs at approximately 7 frames per second on PC and yields promising results. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Fuzzy sets

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1st international Conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

1st International Conference on Computational Intelligence f...

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1st international Conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

ISBN: (纸本)9789811984921

The proceedings contain 68 papers. The special focus in this conference is on Computational Intelligence for Engineering and Management Applications. The topics include: strokes-Related Disease Prediction Using machine learning Classifiers and Deep Belief Network Model;analysis and Detection of Fraudulence Using machine learning Practices in Healthcare Using Digital Twin;prediction and Analysis of Polycystic Ovary Syndrome Using machine learning;feature Selection for Medical Diagnosis Using machine learning: A Review;convolutional Neural Network Architectures Comparison for X-Ray Image Classification for Disease Identification;Secure Shift-Invariant ED Mask-Based Encrypted Medical Image Watermarking;fusion-Based Feature Extraction Technique Using Representation learning for Content-Based Image Classification;a Comparative study on Challenges and Solutions on Hand Gesture recognition;design a Computer-Aided Diagnosis System to Find Out Tumor Portion in Mammogram Image with Classification Technique;a Novel Soft-Computing Technique in Hydroxyapatite Coating Selection for Orthopedic Prosthesis;performance Analysis of Panoramic Dental X-Ray Images Using Discrete Wavelet Transform and Unbiased Risk Estimation;efficient Image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features;texture and Deep Feature Extraction in Brain Tumor Segmentation Using Hybrid Ensemble Classifier;A Systematic Review on Sentiment Analysis for the Depression Detection During COVID-19 Pandemic;vehicular Adhoc Networks: A Review;the data Vortex Switch Architectures—A Review;survey on Genomic Prediction in Biomedical Using Artificial Intelligence;a Brief Review on Right to Recall Voting System Based on Performance Using machine learning and Blockchain Technology;sentiment Analysis Techniques: A Review;network Traffic Classification Techniques: A Review;remote Production Monitoring System.

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learning decision trees and tree automata for a syntactic pattern recognition task

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1st Iberian Conference on pattern recognition and Image Analysis, IbPRIA 2003

作者： Sempere, José M. López, Damián Departamento de Sistemas Informáticos y Computación Universidad Politécnica de Valencia Valencia Spain

ISBN: (纸本)3540402179

Decision trees have been widely used for different tasks in artificial intelligence and data mining. Tree automata have been used in pattern recognition tasks to represent some features of objects to be classified. Here we propose a method that combines both approaches to solve a classical problem in pattern recognition such as Optical Character recognition. We propose a method which is organized in two stages: (1) we use a grammatical inference technique to represent some structural features of the characters and, (2) we obtain edit distances between characters in order to design a decision tree. The combination of both methods benefits from their individual characteristics and is formulated as a coherent unifying strategy. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Decision trees

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