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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371-380 订阅

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Prediction of Gender and Age from Inertial Sensor-based Gait dataset 8

Prediction of Gender and Age from Inertial Sensor-based Gait...

引用

Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Khabir, Kanij Mehtanin Siraj, Md Sadman Ahmed, Masud Ahmed, Mosabber Uddin Univ Dhaka Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781728107882

The technology pertaining to gait generation, recognition and analysis is getting more sophisticated each day. The global demand for a gait-based dataset and its subsequent recognition, leading to extraction of valuable information is now higher than ever. However, inertial sensor-based gait dataset is a comparatively new addition to the field of gait analysis. Consequently, most of the research works incorporating machine learning algorithms into gait dataset are image based. In addition to that, most of the gait-based datasets have been analyzed for gait recognition. There remains very little research work on personal authentication from inertial sensor-based gait dataset. Personal authentication is of several types out of which, predicting gender and age is quite challenging. In this paper, we have tried to face these challenges and have manifested the process of predicting gender and age from the inertial sensor-based gait dataset which is a part of the vast Osaka University-ISIR Gait database. Finally, we have found that the models, Support Vector machine shows the highest accuracy for the problem of classifying gender and Decision Trees shows the highest variance score (R-2 value) for the problem of predicting age.

关键词： gait inertial sensor supervised machine learning gender and age estimation classification and regression

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Assessing reviewers' performance based on mining problem localization in peer-review data

Assessing reviewers' performance based on mining problem loc...

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3rd international conference on Educational data mining, EDM 2010

作者： Xiong, Wenting Litman, Diane Schunn, Christian Department of Computer Science University of Pittsburgh United States Learning Research and Development Center University of Pittsburgh United States

ISBN: (纸本)9780615375298

Current peer-review software lacks intelligence for responding to students' reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system that generates assessment on reviewers' reviewing skills regarding the issue of problem localization. We take a data mining approach, using standard supervised machine learning to build classifiers based on attributes extracted from peer-review data via Natural Language Processing techniques. Our work successfully shows it is feasible to provide intelligent support for peer-review systems to assess students' reviewing performance fully automatically.

关键词： data mining

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A Genetic Based Wrapper Feature Selection Approach Using Nearest Neighbour Distance Matrix

A Genetic Based Wrapper Feature Selection Approach Using Nea...

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3rd conference on data mining and Optimization (DMO)/1st Multi conference on Artificial Intelligence Technology (MCAIT)

作者： Sainin, Mohd Shamrie Alfred, Rayner Univ Utara Malaysia Dept Comp Sci Coll Arts & Sci Sintok Kedah Malaysia Univ Malaysia Sabah Sch Engn & Informat Technol Kota Kinabalu Malaysia

ISBN: (纸本)9781612842127

Feature selection for data mining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI machine learning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.

关键词： machine learning data mining data mining optimization nearest neighbour distance matrix classification feature selection genetic algorithm

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Interactive pattern recognition in Cardiovascular Disease Management. A Process mining approach 3

Interactive Pattern Recognition in Cardiovascular Disease Ma...

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3rd IEEE EMBS international conference on Biomedical and Health Informatics (IEEE BHI)

作者： Fernandez-Llatas, Carlos Bayo, Jose Luis Martinez-Romero, Alvaro Benedi, Jose Miguel Traver, Vicente Univ Politecn Valencia ITACA Valencia Spain Univ Politecn Valencia PRHLT Valencia Spain

ISBN: (纸本)9781509024551

Cardiovascular diseases are one of the main causes of mortality in the European Union. Cardiovascular diseases require a continuous follow-up in order to prevent complications that produce a decrease on the quality of life of patients and can even lead to their death. According to the Evidence Based Medicine thesis, the involvement of the patient in the care process will improve the Quality of Care provided and the efficiency of Care Plans. However, there is controversy in the literature about the fact that the Care Plans that Evidence Based Medicine provides are based on strict clinical cases to generate scientific evidence that is unclear whether they can be applied to personalized medicine. The use of an Interactive pattern recognition approach can create models controlled by human experts based on the biomedical data provided, in addition to medical decisions and the patient personality, providing a better way to find more tailored models. However, the use of Interactive pattern recognition techniques still requires the application of Human Understandable pattern recognition techniques. In this paper, an Interactive pattern recognition technique based on Process mining technology is presented as a solution to deal with this problem.

关键词： Diseases

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Handwritten Character recognition Using Histograms of Oriented Gradient Features in Deep learning of Artificial Neural Network

Handwritten Character Recognition Using Histograms of Orient...

引用

3rd international conference on IT Convergence and Security (ICITCS)

作者： Iamsa-at, Suthasinee Horata, Punyaphol Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen Thailand

ISBN: (纸本)9781479928453

Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ability of two classifiers: Deep-learning Feedforward-Backpropagation Neural Network (DFBNN) and Extreme learning machine (ELM). Three data sets: Thai handwritten characters, Bangla handwritten numerals, and Devanagari handwritten numerals were studied. Each data set was divided into two categories: non-extracted and extracted features by Histograms of Oriented Gradients (HOG). The experimental results showed that using HOG to extract features can improve recognition rates of both of DFBNN and ELM. Furthermore, DFBNN provides higher slightly recognition rates than those of ELM.

关键词： Handwritten Character recognition Feature extraction Histograms of Oriented Gradients Deep learning Neural Network Extreme learning machine

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The research of data mining based on extension sets

The research of data mining based on extension sets

引用

3rd international conference on Information Technology and Applications

作者： Lu, Q Yu, YQ Guangdong Univ Technol Inst Intelligent Engn Guangzhou 510090 Peoples R China

ISBN: (纸本)0769523161

Extension data mining is a new method that is based on the extension analysis method of Extenics. Extenics is a new disciplinary and a new branch of arfiticial intelligence. data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization. This paper presents how to deal with multiple data formats and unify data representation based on extenics. Keyword: matter-element, databases, extension data mining, association rules.

关键词： matter-element databases extension data mining association rules

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2D and 3D Palmprint recognition using Deep learning Method 3

2D and 3D Palmprint Recognition using Deep Learning Method

引用

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

作者： Samai, Djamel Bensid, Khaled Meraoumia, Abdallah Taleb-Ahmed, Abdelmalik Bedda, Mouldi Univ Ouargla Fac Nouvelles Technol Informat & Commun Lab Genie Elect Ouargla 30000 Algeria Univ Larbi Tebessi LAb Math Informat & Syst LAMIS Tebessa 12002 Algeria Univ Valenciennes UMR CNRS 8520 IEMN Lab Valenciennes France Aljouf Univ Coll Engn Elect Engn Dept Sakakah Saudi Arabia

ISBN: (纸本)9781538642382

In the last years, several researchers have interested in two-dimensional (2D) palmprint recognition. In order to enhance the security of biometric systems, recently, some works proposed to use three-dimensional (3D) palmprint recognition. The advantage of using the 3D capture systems is that they capture the 2D and 3D palmprint at the same time, and they give different and complementary information. The 3D component contains the depth of the palm surface, whereas the 2D component contains the texture. In this paper, we proposed an efficient biometric identification system combining 2D and 3D palmprint by fusing them at matching score level. To exploit the 3D palmprint data, we converted it to grayscale images by using the Mean Curvature (MC) and the Gauss Curvature (GC). Feature extraction is made by a deep learning algorithm that is called the Discrete Cosine Transform Net (DCTNet). The experiments performed on a database containing 8000 samples show that the proposed scheme can achieve a high recognition rate.

关键词： Biometrics identification palmprint DCTNet data fusion

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Police data mining and Classification Based on PDCSC Algorithm in international Police Cooperation 24

Police Data Mining and Classification Based on PDCSC Algorit...

引用

3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Du, Qian Sun, Hongjian Shandong Police Coll Foreign Language Teaching & Researching Dept Jinan Shandong Peoples R China

ISBN: (纸本)9798400709777

Aiming at the problems of low data collaboration rate and weak storage capacity of police surveillance data in national border police cooperation, the research is based on Parallel Distributed Clustering with Semantic Constraints (PDCSC) algorithm. Police surveillance data processing model is constructed. The research firstly utilizes local density clustering algorithm to mine and classify the surveillance data, and then introduces the concept of parallelism and constructs the processing model using PDCSC algorithm. The results show that the clustering purity of police surveillance data based on PDCSC method is 92.37% and the data clustering accuracy is 93.67%. Meanwhile, the surveillance data summary of PDCSC method is 7108 which is 1085 and 2241 higher than that of DBSCAN and K-means. This indicates that the PDCSC algorithm can effectively process and classify police surveillance data, providing accurate police incident identification and analysis. The research aims to provide strong support for international police cooperation and improve the efficiency and accuracy of police work.

关键词： international police cooperation PDCSC algorithm Surveillance data data mining data classification

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mining Process Control data Using machine learning

Mining Process Control Data Using Machine Learning

引用

international conference on Computers and Industrial Engineering (CIE39)

作者： Nasr, Emad S. Abouel Al-Mubaid, Hisham Helwan Univ Mech Eng Dept Cairo Egypt Univ Houston Clear Lake City Dept Comp Sci Houston TX USA

ISBN: (纸本)9781424441358

Manufacturing process data collected over time are considered time-series data and can be arranged into control charts. Important applications can be centered around these data like, for example, recognition of specific patterns, pattern similarity, detecting anomalies, and clustering and classification of patterns. We study and evaluate a number of classification techniques for process control data. For pattern similarity, we examine distance measure with raw data and with new feature extracted from the data. The evaluation is conducted with common benchmark process control data for time series process variables. This paper shows that data mining and machine learning can be extremely beneficial in acquiring and producing knowledge and discoveries form process data to benefit the industry.

关键词： process control data mining process data data mining

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Efficient multi-method rule learning for pattern classification machine learning and data mining

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2nd international conference on pattern recognition and machine Intelligence

作者： Maiti, Chinmay Pal, Somnath Jadavpur Univ Dept Info Tech Kolkata 700032 W Bengal India Bengal Engn & Sci Univ Dept Comp Sci & Tech Howrah Bengal India

ISBN: (纸本)9783540770459

The work presented here focuses on combining multiple classifiers to form single classifier for pattern classification, machine learning for expert system, and data mining tasks. The basis of the combination is that efficient concept learning is possible in many cases when the concepts learned from different approaches are combined to a more efficient concept. The experimental result of the algorithm, EMRL in a representative collection of different domain shows that it performs significantly better than the several state-of-the-art individual classifier, in case of 11 domains out of 25 data sets whereas the state-of-the-art individual classifier performs significantly better than EMRL only in 5 cases.

关键词： machine learning multiple classifiers missing values discretization classification

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