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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是241-250 订阅

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learning with segment boundaries for hierarchical HMMs

Learning with segment boundaries for hierarchical HMMs

引用

3rd international conference on Advances in pattern recognition

作者： Gotou, N Hayashi, A Suematu, N Hiroshima City Univ Grad Sch Informat Asaminami Ku Hiroshima 7313194 Japan

ISBN: (纸本)3540287574

Hierarchical hidden Markov models (HHMMs) can be used for time series segmentation. However, it is difficult to obtain a desirable segmentation result, because the form of learning for HHMMs is unsupervised. In the paper, we present a semisupervised learning algorithm for HHMMs. It is semisupervised in the sense that the supervisor teaches segmentation boundaries but not segment labels. The learning performance of the proposed algorithm is demonstrated through an experiment using music data.

关键词： learning systems

来源：评论

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Research on Application of Tobacco Company Supply Chain Management Based on data mining Technology 3

Research on Application of Tobacco Company Supply Chain Mana...

引用

3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Yan, Dazhu Kunming Tobacco Co Xishan Branch Kunming 650000 Yunnan Peoples R China

ISBN: (纸本)9781665417907

As the market economy becomes more diversified, competition among enterprises is increasing. As a technological product in the information age, data mining technology is applied to enterprise supply chain management to help improve management efficiency and enhance the core competitiveness of enterprises. In this regard, this article takes a tobacco company as an example, briefly explains the basic principles of data mining technology in the application of supply chain management, outlines the supply chain management process based on data mining technology, analyzes and explores the selection of partners based on fuzzy data mining, and aims to give Enterprise supply chain management better apply data mining technology to provide some useful references.

关键词： data mining Technology Tobacco Company Supply Chain Management

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Designing Disease Prediction Model Using machine learning Approach 3

Designing Disease Prediction Model Using Machine Learning Ap...

引用

3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Dahiwade, Dhiraj Patle, Gajanan Meshram, Ektaa Abha Gaikwad Patil Coll Engn Dept Comp Sci Engn Nagpur Maharashtra India

ISBN: (纸本)9781538678084

Now-a-days, people face various diseases due to the environmental condition and their living habits. So the prediction of disease at earlier stage becomes important task. But the accurate prediction on the basis of symptoms becomes too difficult for doctor. The correct prediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has large amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the huge amount of medical data. We proposed general disease prediction based on symptoms of the patient. For the disease prediction, we use K-Nearest Neighbor (KNN) and Convolutional neural network (CNN) machine learning algorithm for accurate prediction of disease. For disease prediction required disease symptoms dataset. In this general disease prediction the living habits of person and checkup information consider for the accurate prediction. The accuracy of general disease prediction by using CNN is 84.5% which is more than KNN algorithm. And the time and the memory requirement is also more in KNN than CNN. After general disease prediction, this system able to gives the risk associated with general disease which is lower risk of general disease or higher.

关键词： CNN KNN machine learning Disease Prediction

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machine learning Approaches for Lung Cancer Prediction 3rd

Machine Learning Approaches for Lung Cancer Prediction

引用

3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Nagamuruganandam, A. Chandran, C.P. Rajathi, S. Agilandeswari, V. Madurai Kamaraj University TN Madurai625021 India Ayya Nadar Janaki Ammal College TN Sivakasi626124 India M.V.Muthiah Govt. Arts College for Women TN Dindugul624001 India

ISBN: (纸本)9783031862922

Lung cancer is one of the most fatal types of cancer, therefore, the early and accurate diagnosis can greatly improve the patients’ quality of life, as well as the survival rate. In this research, the progress and the use of different forms of ML, sophistications such as deep learning and ensemble methods, and Support Vector machines: Early Detection of Lung Cancer using Integrated Medical Imaging data from CT and x-ray, Clinical data, and Demographics. These models’ performances have therefore been evaluated by considering factors like accuracy, precision, recall, and even the AUC-ROC with regard to the ability of set apart between the malignant and the benign. The results presented in this work prove the significance of rich annotations and feature selection as an essential step in the improvement of model’s interpretability and accuracy. There are supposed effects of integrating the prediction models into the clinical processes as a way to diminishing lung cancer death rates, facilitated by improved methods of early detection and treatment planning. However, before such models are used in day to day practice, the concept requires more validation and research, and also recognition from the relevant authority. It is considered as a part of emerging literature proving the applicability of the ML in oncology and advocating for increased research and joint work of physicians and data scientists to enhance the lung cancer prediction models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Improving high-resolution satellite images retrieval using Linear SVM classifier and data augmentation 3

Improving high-resolution satellite images retrieval using L...

引用

3rd international conference on pattern Analysis and Intelligent Systems (PAIS)

作者： Sebai, Houria Kourgli, Assia USTHB Fac Elect & Informat LTIR BP 32 El Alia 16111 Bab Ezzouar Algeria

ISBN: (纸本)9781538642382

The availability of huge remote sensing image dataset imposes recourse to powerful techniques of content-based image retrieval for archiving and mining. This paper propose descriptors based on the SIFT (Scale invariant features) combined with SVM linear classification. To build a powerful image classifier using very little training data, image augmentation is usually required to boost the performance of the classification. For this reason, an augmentation data is used to increase the training data for the SVM (Support vector machine) classifier. The creation of the training data is done using several techniques of augmentation: anisotropic filter. We report a first evaluation of the CBIR (Content based image retrieval) and the second evaluation of the system aims to compare the deep learning with the boosted SVM classification.

关键词： CBIR SIFT SVM classifier descriptors data augmentation

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Application of text mining technology in power data prediction 3

Application of text mining technology in power data predicti...

引用

2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Guo, Jian Guo, Wu College of Architecture and Civil Engineering Qiqihar University Qiqihar China College of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783800758760

This paper analyses the main research methods of power text mining technology in detail, and discusses the research hotspots of power text named entity recognition and named entity relationship extraction based on machine learning and deep learning. On this basis, the limited Boltzmann machine is further used to predict the power data. The test results show that the analysis of power text features in this paper is accurate, can effectively analyse the key text information, and the prediction results meet the requirements. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Character recognition

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Simple mimetic classifiers

引用

3rd international conference on machine learning and data mining in pattern recognition

作者： Estruch, V Ferri, C Hernández-Orallo, J Ramírez-Quintana, MJ Univ Politecn Valencia DSIC Valencia 46020 Spain

ISBN: (纸本)3540405046

The combination of classifiers is a powerful tool to improve the accuracy of classifiers, by using the prediction of multiple models and combining them. Many practical and useful combination techniques work by using the output of several classifiers as the input of a second layer classifier. The problem of this and other multi-classifier approaches is that huge amounts of memory are required to store a set of multiple classifiers and, more importantly, the comprehensibility of a single classifier is lost and no knowledge or insight can be acquired from the model. In order to overcome these limitations, in this work we analyse the idea of "mimicking" the semantics of an ensemble of classifiers. More precisely, we use the combination of classifiers for labelling an invented random dataset, and then, we use this artificially labelled dataset to re-train one single model. This model has the following advantages: it is almost similar to the highly accurate combined model, as a single solution it requires much fewer memory resources, no additional validation test must be reserved to do this procedure and, more importantly, the resulting model is expressed as a single classifier in terms of the original attributes and, hence, it can be comprehensible. First, we illustrate this methodology using a popular data-mining package, showing that it can spread into common practice, and then we use our system SMILES, which automates the process and takes advantage of its ensemble method.

关键词： multi-classifier systems stacking decision trees comprehensibility in machine learning rule extraction

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Implementation of machine learning Algorithms For Human Activity recognition 3

Implementation of Machine Learning Algorithms For Human Acti...

引用

3rd international conference on Signal Processing and Communication (ICPSC)

作者： Vijayvargiya, Ankit Kumari, Nidhi Gupta, Palak Kumar, Rajesh Malviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Swami Keshvanand Inst Technol Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781665428644

Human Activity recognition (HAR) is technically the problem of forecasting an individual's actions based on evidence of their gesture using sensors functioning as accelerometer and gyroscope. It plays a major role in contrasting sectors such as personal biometric signature, daily life monitoring, anti-terrorists along with anti-crime securities, medical-related applications, and so on. These days, smart phones are well-resourced with leading processors and built-in sensors. This comes up with the possibility to unfold a new arena of data mining. This paper signifies the analysis of HAR focused on data composed via accelerometer sensors of smart phones. Further, it illustrates the use of time-domain features which are acquired with the help of a windowing approach termed as overlapping. It is accompanied by a window size of 250ms along with overlapping of 25%. Numerous machine learning classifiers such as k-nearest neighbors, linear discriminant analysis, bagging classifier, gradient boosting classifier, decision tree, random forest, and support vector machine using three different kernels were practiced. The outcomes exhibit that random forest with 5-fold cross-validation imparts the highest accuracy (92.71%) in recognition of human activities.

关键词： Human Activity recognition machine learning Overlapping Windowing Feature Extraction

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Beyond Concise and Colorful: learning Intelligible Rules 3

Beyond Concise and Colorful: Learning Intelligible Rules

引用

3rd international conference on Knowledge Discovery and data mining, KDD 1997

作者： Pazzani, Michael J. Mani, Subramani Shankle, W. Rodman Department of Information and Computer Science The University of California IrvineCA92697 United States Department of Neurology The University of California IrvineCA92697 United States

ISBN: (纸本)1577350278

A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful categories. However, research in data mining has not paid attention to the cognitive factors that make learned categories intelligible to human users. We show that one factor that influences the intelligibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind. Copyright © 1997, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Signal processing

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Intrusion Detection using data mining: A contemporary comparative study 3

Intrusion Detection using Data Mining: A contemporary compar...

引用

3rd international conference on pattern Analysis and Intelligent Systems (PAIS)

作者： Bouteraa, Imad Derdour, Makhlouf Ahmim, Ahmed Univ Larbi Tebessi Dept Comp Sci Tebessa Algeria Univ Larbi Tebessi LAMIS Lab Tebessa Algeria Badji Mokhtar Univ Annaba LRS Lab Annaba Algeria

ISBN: (纸本)9781538642382

Intrusion detection systems play a crucial rule in this era where networks reached almost any sector. Unfortunately, intrusion detection systems are far from perfectness. Therefore, researchers never stopped digging deeper to improve them. In this context, data mining techniques have been highly exploited for intrusion detection. In this paper, we present a comparative study of data mining techniques for intrusion detection. Specifically, we study the overall performances of those methods as well as the impact of training data size on their results. We use ISCX2012 as a benchmark for our experimentation. A realistic dataset that represents at a certain level today's network traffic. The study shows that relatively old methods outperform some of the techniques highly used actually by the community. Regarding the impact of training dataset size, the investigated methods react differently from each other when we add more data to the training dataset. In addition, the results highlight the importance of attack traffic in the training dataset. Moreover, they strongly suggest the use of Random Forest for intrusion detection due to its linear performance relation with the training dataset's size.

关键词： Intrusion detection data mining machine learning ISCX2012

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