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

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Design and Implementation of Office Automation System based on Web Service Framework and data mining Techniques

Design and Implementation of Office Automation System based ...

引用

2016 3rd international conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： He Huang Sichuan Vocational and Technical College Suining City

Smart mobile devices, the application of the social network media and the information behavior main body of increasingly frequent activity, has produced in these types of equipment, media and behavior diversity, complex structure, a large number of unstructured data and semi-structured data quantity has increased dramatically, human data into a driving force for social development is one of the era of big data. Under this background, we conduct research on office automation system based on web service framework and data mining techniques. We design and implement the system with the assistance of machine learning algorithms as well as the web service framework and achieve the satisfactory result which serves as the foundation for our future research.

关键词： Office Automation Web Service data mining Systematic Implementation

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Unsupervised mining of Under-resourced Speech Corpora for Tone Features Classification

Unsupervised Mining of Under-resourced Speech Corpora for To...

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international Joint conference on Neural Networks

作者： Moses E. Ekpenyong Udoinyang G. Inyang Department of Computer Science University of Uyo

ISBN: (纸本)9781509006212

In this contribution, the unsupervised mining of speech corpora for the efficient classification of tone features was investigated. Input vectors to the experiment were generated from tone pattern alignments of Ibibio (Benue-Congo, Nigeria) corpus. The corpus used for the experiment contained 16,905 words/phrases. The proposed system design is novel, and integrates two unsupervised tools - k-means clustering and self organizing map (SOM) model, into a methodological workflow, that evaluates and selects the optimal number of clusters with the subsequent association of each clustering point to the input data points. In order to reduce data dimensionality for effective visualization, a non-negative matrix factorization (NMF) was introduced to rid the k-means clusters of noisy attributes. The k-means cluster points generated by the optimum clusters (two in this case) were evaluated by the Silhouette algorithm and finally fed into the SOM, to improve the efficiency of features classification. Results obtained validate existing research claims and demonstrates the importance of vowel-only features in the recognition of tone patterns. A SOM visualization of the input vectors revealed that vowel-only feature correlates better with other input vectors such as syllable and phoneme, compared to consonant-only features. Furthermore, clustering the input datasets into the optimal number of clusters enabled proper and timely visualization of the map. This contribution is therefore vital for advancing future speech processing research on under-resourced languages.

关键词： data mining K-means clustering machine learning SOM visualization Under-resourced language Unsupervised learning Unsupervised learning data mining self-organising feature maps optimum number GRHL3 gene Artificial Intelligence machine learning mining Visualization pitch Speech

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Traffic Speed Prediction and Congestion Source Exploration: A Deep learning Method

Traffic Speed Prediction and Congestion Source Exploration: ...

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IEEE international conference on data mining (ICDM)

作者： Jingyuan Wang Qian Gu Junjie Wu Guannan Liu Zhang Xiong School of Computer Science and Engineering Beihang University Beijing China Beihang University Beijing CN School of Economics and Management Beihang University Beijing China Research Institute of Beihang University in Shenzhen Shenzhen China

ISBN: (纸本)9781509054749

Traffic speed prediction is a long-standing and critically important topic in the area of Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. Traffic speed prediction, however, is still in its initial stage without making full use of spatio-temporal traffic information. In light of this, in this paper, we propose a deep learning method with an Error-feedback Recurrent Convolutional Neural Network structure (eRCNN) for continuous traffic speed prediction. By integrating the spatio-temporal traffic speeds of contiguous road segments as an input matrix, eRCNN explicitly leverages the implicit correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback neurons to the recurrent layer, eRCNN learns from prediction errors so as to meet predictive challenges rising from abrupt traffic events such as morning peaks and traffic accidents. Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the strong predictive power of eRCNN in comparison to some state-of-the-art competitors. The necessity of weight pre-training using a transfer learning notion has also been testified. More interestingly, we design a novel influence function based on the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing.

关键词： Neurons Roads Convolution machine learning Biological neural networks Feature extraction Training

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data Field-based Support Vector machine for Image Classification

Data Field-based Support Vector Machine for Image Classifica...

引用

2016 3rd international conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Yi Lin Computer School Wuhan University

In computer vision, object recognition is still a challenge. In this paper, a new method based on data field is proposed for image object classification with color histograms and diffusion distances. Among them, the topological potential is used to select the optimal parameters. The experimental results show that the proposed method has better accuracy and shorter run time than four common kernels. It not only overcomes the drawbacks of the existing parameter selection method, but also coincident with Vapnik’s theory, which theoretically guarantees the generalization of learning machines.

关键词： Image Classification data field topological Potential Diffusion distance SVM

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Detection and classification of diseases of Grape plant using opposite colour Local Binary pattern feature and machine learning for automated Decision Support System

Detection and classification of diseases of Grape plant usin...

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international conference on Signal Processing and Integrated Networks (SPIN)

作者： Harshal Waghmare Radha Kokare Yogesh Dandawate Dept. of Electronics and Telecommunication Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9781467391986

Plant diseases cause major economic and production losses as well as curtailment in both quantity and quality of agricultural production. Now a day's, for supervising large field of crops there is been increased demand for plant leaf disease detection system. The critical issue here is to monitor the health of the plants and detection of the respective diseases. Studies show that most of the plant disease can be diagnosed from the properties of the leaf. Thus leaf based disease analysis for plants is an exciting new domain. The technique proposed for identification of plant disease through the leaf texture analysis and pattern recognition. In this work we focus on Grapes plant leaf disease detection system. The system takes a single leaf of a plant as an input and segmentation is performed after background removal. The segmented leaf image is then analyzed through high pass filter to detect the diseased part of the leaf. The segmented leaf texture is retrieved using unique fractal based texture feature. Fractal based features are locally invariant in nature and therefore provides a good texture model. The texture of every independent disease will be different. The extracted texture pattern is then classified using multiclass SVM. The work classifies focus on major diseases commonly observed in Grapes plant which are downy mildew & black rot. The proposed approach avails advice of agricultural experts easily to farmers with the accuracy of 96.6%.

关键词： Diseases Feature extraction Support vector machines Image color analysis Agriculture Pipelines Decision support systems

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Impact of Baggingon MLP Classifier for Credit Evaluation

Impact of Baggingon MLP Classifier for Credit Evaluation

引用

international conference on Computing for Sustainable Global Development

作者： Shashi Dahiya S. S. Handa N. P. Singh Manav Rachna International University Faridabad Manav Rachna International University Management Development Institute (MDI)

ISBN: (纸本)9781467394178

Credit risk is one of the critical and the major challenges faced by banks. Its evaluation is an important factor in decision making for grant of credit. The accuracy in credit risk evaluation techniques adds to the profitability of banks and avoids financial loss. data mining has played a very significant role in evaluating credit risk. Multi-Layer Perception Neural Network has been used individually and in combination with other machine learning or statistical techniques to build very efficient hybrid classifiers for credit risk evaluation. In this study, three different models are developed - Single MLP model, Feature Selection based MLP model and Bagging based hybrid MLP model. The results shows that the accuracy of single MLP classifier is significantly enhanced by using suitable feature selection method and is further enhanced by using the ensemble based bagging technique on MLP. The results also revealed that 70:30 percentage split of dataset contributed to the performance of all the classifiers.

关键词： Bagging Classifiers Credit Risk Ensemble Technique Feature Selection Hybrid Models

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Extracting Clinical entities and their assertions from Chinese Electronic Medical Records Based on machine learning

Extracting Clinical entities and their assertions from Chine...

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2016 3rd international conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Jianhong Wang Yousong Peng Bin Liu Zhiqiang Wu Lizong Deng Taijiao Jiang College of Computer Science and Electronic Engineering Hunan University Center of System Medicine Institute of Basic Medical SciencesChinese Academy of Medical Sciences & Peking Union Medical College

With the rapid growth of electronic medical records（EMRs） in China, large amounts of clinical data have been accumulated. However, limited work for extracting information from EMRs in Chinese has been conducted. In this work, using manually annotated dataset of EMRs in Chinese, we investigated the clinical Named Entities recognition（NER） based on Conditional Random Field（CRF） and further built a Support Vector machine（SVM） classifier to determine their assertion status and evaluate the contributions of different features for assertion classification. For Chinese clinical NER, our CRF-based classifier achieved the best F-measure of 89.07%, while the SVM-based assertion classifier achieved a maximum F-measure of 94.10%. Our work suggests that machine learning methods are helpful in NER and assertion determination for Chinese medical clinical records.

关键词： Chinese Electronic Medical Records Information extraction Named Entity recognition assertion classification machine learning

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An Empirical Comparison of Support Vector machines Versus Nearest Neighbour Methods for machine learning Applications 3rd

An Empirical Comparison of Support Vector Machines Versus Ne...

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3rd international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Gamboni, Mori Garg, Abhijai Grishin, Oleg Oh, Seung Man Sowani, Francis Spalvieri-Kruse, Anthony Toussaint, Godfried T. Zhang, Lingliang New York Univ Abu Dhabi Fac Sci POB 129188 Abu Dhabi U Arab Emirates

ISBN: (纸本)9783319255309;9783319255293

Support vector machines (SVMs) are traditionally considered to be the best classifiers in terms of minimizing the empirical probability of misclassification, although they can be slow when the training datasets are large. Here SVMs are compared to the classic k-Nearest Neighbour (k-NN) decision rule using seven large real-world datasets obtained from the University of California at Irvine (UCI) machine learning Repository. To counterbalance the slowness of SVMs on large datasets, three simple and fast methods for reducing the size of the training data, and thus speeding up the SVMs are incorporated. One is blind random sampling. The other two are new linear-time methods for guided random sampling which we call Gaussian Condensing and Gaussian Smoothing. In spite of the speedups of SVMs obtained by incorporating Gaussian Smoothing and Condensing, the results obtained show that k-NN methods are superior to SVMs on most of the seven data sets used, and cast doubt on the general superiority of SVMs. Furthermore, random sampling works surprisingly well and is robust, suggesting that it is a worthwhile preprocessing step to either SVMs or k-NN.

关键词： machine learning data mining Support vector machines SMO Training data condensation k-Nearest neighbour methods Blind and guided random sampling Wilson editing Gaussian Condensing

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mining positional data streams 3

Mining positional data streams

引用

3rd international Workshop on New Frontiers in mining Complex patterns, NFMCP 2014 held in Conjunction with European conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML-PKDD 2014

作者： Haase, Jens Brefeld, Ulf Knowledge Mining and Assessment Group TU Darmstadt and DIPF Darmstadt Germany

ISBN: (纸本)9783319178752

We study frequent pattern mining from positional data streams. Existing approaches require discretised data to identify atomic events and are not applicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline. © Springer international Publishing Switzerland 2015.

关键词： data mining

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Recent Advances in Information and Communication Technology 2015: Proceedings of the 11th international conference on Computing and Information Technology (IC2IT) 1

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丛书名： Advances in Intelligent Systems and Computing 361

2015年

作者： Herwig Unger, Phayung Meesad, Sirapat Boonkrong (eds.)

ISBN: (数字)9783319190242

ISBN: (纸本)9783319190235;9783319190242

This book presents recent research work and results in the area of communicationand information technologies. The book includes the main results of the 11th internationalconference on Computing and Information Technology (IC2IT) held during July2nd-3rd, 2015 in Bangkok, Thailand. The book is divided into the two main parts datamining and machine learning as well as data Network and *** algorithms and methods of data mining asr discussed as well as innovativeapplications and state-of-the-art technologies on data mining, machine learning anddata networking.

关键词： data mining machine learning Computer networks

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