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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1150 条记录，以下是121-130 订阅

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Remembering similitude terms in CBR

引用

3rd international conference on machine learning and data mining in Pattern Recognition

作者： Armengol, E Plaza, E CSIC Spanish Council Sci Res Artificial Intelligence Res Inst Catalonia 08193 Spain

ISBN: (纸本)3540405046

In concept learning, inductive techniques perform a global approximation to the target concept. Instead, lazy learning techniques use local approximations to form an implicit global approximation of the target concept. In this paper we present C-LID, a lazy learning technique that uses LID for generating local approximations to the target concept. LID generates local approximations in the form of similitude terms (symbolic descriptions of what is shared by 2 or more cases). C-LID caches and reuses the similitude terms generated in past cases to improve the problem solving of future problems. The outcome of C-LID (and LID) is assessed with experiments on the Toxicology dataset.

关键词： learning systems

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A machine learning model for information retrieval with structured documents

引用

3rd international conference on machine learning and data mining in Pattern Recognition

作者： Piwowarski, B Gallinari, P Univ Paris 06 LIP6 F-75015 Paris France

ISBN: (纸本)3540405046

Most recent document standards rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. Only a few models have been proposed for handling structured documents, and the design of such systems is still an open problem. We present here a new model for structured document retrieval which allows to compute and to combine the scores of document parts. It is based on bayesian networks and allows for learning the model parameters in the presence of incomplete data. We present an application of this model for ad-hoc retrieval and evaluate its performances on a small structured collection. The model can also be extended to cope with other tasks such as interactive navigation in structured documents or corpus.

关键词： Information retrieval systems

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Performing an assortment of tasks on machine learning and Benchmarking based Clinical Time Series data 3

Performing an assortment of tasks on Machine Learning and Be...

引用

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

作者： Ramya, P. Geetha, G. Sindhura, V VR Siddhartha Engn Coll Dept IT Vijayawada Kanuru India

ISBN: (纸本)9781538678084

Conceptual Health care is one of the most exciting borders in data mining and machine learning. Appropriation of electronic health records (EHRs) made a blast in advanced clinical information which is accessible for examination, but progress in machine learning for healthcare research has been complicated to measure because of the absence of openly available benchmark data sets. In this paper we propose three clinical expectation benchmarks to overcome the issue of utilizing the information got from the freely accessible Medical Information Mart for Intensive Care (Emulate III) database. These assignments cover a scope of clinical issues counting demonstrating danger of mortality, anticipating length of remain and distinguishing physiologic decay. MIMIC-III (Medical Information Mart for Intensive Care III) is a considerable, openly accessible database containing de-identified wellbeing related information related with more than forty thousand patients who remained in basic consideration units of the Beth Israel Deaconess Medical Center somewhere in the range of 2001 and 2012. Our plan is to perform various tasks with an objective to mutually take in a variety of clinically important forecast assignments based on similar time arrangement information.

关键词： Mortality Prediction LOS machine learning Deep learning classification

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Congestion Detection in Wireless Sensor Networks Using MLP and Classification by Regression 3

Congestion Detection in Wireless Sensor Networks Using MLP a...

引用

3rd international conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Madalgi, Jayashri B. Kumar, S. Anupama VTU Research Scholar Gogte Institute of technology RV College of Engineering Bangalore Karnataka IN

ISBN: (纸本)9781538611449

Wireless Sensor Network (WSN) is network of hundreds or thousands of sensors. Congestion occurs in wireless sensor networks when all the sensors nearby event start sending data to the base station. Congestion results in less throughput and non reliability of a system. The machine learning algorithms can be applied for congestion detection in network and then congestion can be mitigated by lowering the transmission rate. In this paper we analyze the performance of multilayer level perception (MLP) a neural network technique and classification by regression algorithms. The machine learning techniques are applied to detect the different levels of congestion in as low, medium or high. It is found that classification by regression is more efficient than MLP in detecting the congestion for the generated data set of WSN simulation using NS2.

关键词： machine learning MLP regression classification Congestion control in WSN data mining

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Based on the Application of machine learning Algorithms in Business data Analysis 3

Based on the Application of Machine Learning Algorithms in B...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Yiwen The University of Auckland Auckland Cbd Auckland1010 New Zealand

ISBN: (纸本)9798350319125

With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machine learning algorithm has been widely used in the field of business data analysis. This paper will introduce the application of machine learning algorithm in business data analysis, including classification, clustering, prediction and association rule mining. © 2023 IEEE.

关键词： artificial intelligence Business data analysis commercial market machine learning algorithms

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Designing Disease Prediction Model Using machine learning Approach 3

Designing Disease Prediction Model Using Machine Learning Ap...

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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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Prediction of Churning Behavior of Customers in Telecom Sector Using Supervised learning Techniques 3

Prediction of Churning Behavior of Customers in Telecom Sect...

引用

3rd IEEE international conference on Computing, Communication and Security (ICCCS)

作者： Ali, Muhammad Rehman, Aziz Ur Hafeez, Shamaz Super Coll Lahore Pakistan

ISBN: (纸本)9781538662274

data mining is vast area that co-relates diverse branches i.e Statistics, data Base, machine learning and Artificial intelligence. Various applications are accessible in various areas. Churning of the Customer is the behavior when client never again needs to stay with his association with the company. Customer Churn Management is assuming essential job in client management. Nowadays different telecommunication companies are concentrating on distinguishing high esteemed and potential churning clients to expand benefit and share market. It is comprehended that making new clients are costlier than to holding existing client. There is a current issue that customer leave the organization because of obscure reasons. In our investigation, we predict churn behavior of the client by utilizing diverse data mining methods. It will in the long run help in breaking down client's behavior and characterize whether it is a churning client or not. We utilize online accessible data set available at Kaggle repository and for forecasting of Customer behavior we utilized different algorithms while we achieved 99.8% accuracy level using Bagging Algorithms.

关键词： data mining Weka Supervised learning Customer churn

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Research on Radiation Damage Characteristics of Optical Fiber Materials Based on data mining and machine learning 3rd

Research on Radiation Damage Characteristics of Optical Fibe...

引用

3rd European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid Information Processing (ADHIP)

作者： Li, Ang Wang, Tian-hui Jilin Univ Zhuhai Coll Zhuhai Peoples R China

ISBN: (纸本)9783030364021;9783030364014

In order to better analyze the damage characteristics of fiber materials under radiation environment, combined with data mining algorithm to calculate the degree of damage of material structure damage. Combine with machine learning method to analyze the calculation results, obtain the damage range of fiber material structure, standardize material damage characteristics and Grade, accurately determine the damage of material structure, and finally improve the radiation damage characteristics of fiber materials. Experiments show that the research on radiation damage characteristics of fiber materials based on data mining and machine learning is accurate and reasonable.

关键词： data mining machine learning Fiber material Radiation damage characteristics

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Privacy Preserving in Distributed SVM data mining on Vertical Partitioned data 3

Privacy Preserving in Distributed SVM Data Mining on Vertica...

引用

3rd international conference on Soft Computing and machine Intelligence (ISCMI)

作者： Omer, Mohammed Z. Gao, Hui Sayed, Faisal UESTC Sch Comp Sci & Engn Chengdu 611731 Peoples R China Arab Supplier Gen Trading POB 2894 Dubai U Arab Emirates

ISBN: (纸本)9781509036967

data mining algorithms tacitly quite access to the data either at centralized or distributed form. Distributed data becomes a big challenge and cannot handle by a classical analytic tool. Cloud Computing can solve the issues of processing, storing, and analyzing the data at distributing locations within the cloud. However, a significant problem that is preventing free sharing of data is privacy and security issues, therefore obstructing data mining schemes. Lately, there is increasingly hard to find a solution to these problems. Due to the existing knowledge in a more distributed data and better for data mining issues. An important task of data mining and machine learning is classification, a widely used in classification is support vector machine (SVM) algorithms applicable in many various domains. In this paper, we proposes a privacy-preserving solution for SVM classification. Our workaround constructing a global SVM classification model from vertically partitioned distributed data at multi-parties based on Gram matrix, without revealing a party's data. We proposed an efficient and preserve privacy protocol for SVM classification on vertical partitioned data. Our experimental results, the accuracy of distributed SVM using Gram matrix up to 90% and the privacy not compromised.

关键词： Classification Privacy Preserving Homomorphic Encryption Gram Matrix

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machine learning Approaches for Marketing Campaign in Portuguese Banks 3

Machine Learning Approaches for Marketing Campaign in Portug...

引用

3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Alexandra, Jennifer Sinaga, Kristina Pestaria Bina Nusantara Univ Dept Master Informat Syst Management Jakarta Indonesia

ISBN: (纸本)9781665425803

Bank is a type of business that deals with saving, circulation of money, deposits and others. The number of services provided by banks is very diverse, this depends on the capabilities of each bank. The more capable and better the bank is, the more services it will offer. Introducing the product directly has been commonly used for various industries, one of them is the banking industry. In directly introducing products, banks can conduct market analysis by utilizing the information technology space that can assist in making decisions. By analyzing bank marketing data, it can be used to select the type of marketing to do. Marketing campaigns can be carried out via email, telephone, and direct email to prospective customers that allow potential customers to decide whether to take the product offered or not. With increasing time, the amount of incoming data continues to grow. With this increasing data, one of the bank institutions found it difficult to predict whether their clients would subscribe to a term deposit or not. Therefore, in this paper, the data mining process will be carried out using classification (Decision Tree, Wye Bayes, and Random Forest) and clustering (K-Means, K-Medoids, and DBSCAN) methods to predict if the client will subscribe a term deposit.

关键词： component machine learning data mining Classification Clustering

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