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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1791 条记录，以下是1191-1200 订阅

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Handwritten Devanagari Character recognition using Convolutional Neural Network

Handwritten Devanagari Character Recognition using Convoluti...

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international conference for Convergence of Technology (I2CT)

作者： Aarati Mohite Sushama Shelke College of Engineering Pune India

ISBN: (数字)9781538652329

ISBN: (纸本)9781538652336

In any pattern recognition task, feature extraction and classification stages handle the responsibility of recognizing the patterns accurately. Deep learning relives the task of feature extraction and extracts them automatically, thus reducing the programmer's burden. Deep learning is replacing other pattern recognition techniques recently. In applications like character recognition, which involves large amount of database and variability in the data, deep learning is the right choice to handle the challenges involved. In this paper, a system for handwritten Devanagari character recognition using Convolutional Neural Network is discussed. the recognition accuracy obtained is 91.23% for Devanagari characters and 100% for Devanagari numerals.

关键词： Feature extraction Character recognition machine learning Computer architecture Neurons databases

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machine learning and knowledge discovery in analog circuit fault detection 14

Machine learning and knowledge discovery in analog circuit f...

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14th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Zhang, Chijian Li, Junkai Zhang, Yanxi Liu, Congcong College of Physics Electronic Information Anhui Normal University Wuhu China

ISBN: (纸本)9781538680971

the existing fault diagnosis system focuses on data pre-processing to extract fault features, the fault modeling process is complex, and it is difficult to discover and learn new fault modes during system operation. An analog circuit fault automatic diagnosis system based on CFPNN (Constructive Forward Propagation Neural Networks) was analyzed in this paper. the CFPNN's computation is very small and it can capture all kinds of transient-state fault. the system fault-diagnosis precision is 100% when the circuit fault-feature deviation within ± 4%. this system can set a reject pattern to avoid risk from false-recognition with big disturbance and discover new knowledge (new fault-sample). the system not only learn new testing-sample(new knowledge) and finish information fusion by adding new-sample in the original CFPNN but also without retraining the history samples. the above conclusions are validated by theory and experiments. © 2018 IEEE.

关键词： Failure analysis

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Classification of IRIS dataset using Classification Based KNN Algorithm in Supervised learning

Classification of IRIS Dataset using Classification Based KN...

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international conference on Computing, Communication & Automation (ICCCA)

作者： K thirunavukkarasu Ajay S. Singh Prakhar Rai Sachin Gupta School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781538669488

machine learning is about prediction on unseen data or testing data and a set of algorithms are required to perform task on machine learning. there are three types of machine learning are called as Supervised, Unsupervised and Reinforcement learning. In this paper we have worked on supervised learning. We have taken the iris dataset and used K-Nearest Neighbors (KNN) classification Algorithm. Our purpose is build the model that is able to automatically recognize the iris species. Tools used for this in paper are Numpy, Pandas, Matplotlib and machine learning library.

关键词： Testing Iris recognition machine learning Training machine learning algorithms Training data Iris

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High-Performance Linguistics Scheme for Cognitive Information Processing 4th

High-Performance Linguistics Scheme for Cognitive Informatio...

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4th international conference on Advanced Computing, Networking and Informatics (ICACNI)

作者： Suryanarayana, D. Kanakam, Prathyusha Hussain, S. Mahaboob Gupta, Sumit Vishnu Inst Technol Dept Comp Sci & Engn Bhimavaram Andhra Pradesh India

ISBN: (纸本)9789811033735;9789811033728

Natural language understanding is a principal segment of natural language processing in semantic analysis to the use of pragmatics to originate meaning from context. Information retrieval (IR) is one of the emerging areas to deal with enormous amounts of data, which are in the form of natural language. Content of the query posed will affect both volume of data and design of IR applications. this paper presents a cognition-applied methodology termed as High-Performance Linguistics (HPL), which is a question-answering system for interpreting a natural language sentence/query. It constitutes three phases of computations: parsing, triplet generation and triplet mapping/matching. the generation of the triplets for the knowledge base is to create new data and compare them with that of stored triplets in the database. thus, the generation of the cognitive question-answering system can make easy using this machine learning techniques on the generated triplet database.

关键词： Pragmatics RDF Triplets Ontology Information retrieval Linguistics Semantics Indexing

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recognition of tunnel cracks based on deep convolutional neural network classifier 4th

Recognition of tunnel cracks based on deep convolutional neu...

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4th international conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Yang, Min Song, Qing Xin, Xueshi Yang, Lu Laboratory of Pattern Recognition and Intelligent Vision Beijing University of Posts and Telecommunications Beijing China Beijing Songze Technology Co. Beijing China

ISBN: (纸本)9789811322020

It is of vital importance to obtain timely identification and treatment of tunnel cracks in ensuring railway traffic safety. As the amount of tunnel images collected by high-speed cameras equipped on the train is extremely large, manual method is unable to meet the actual needs in recent years while traditional image processing methods become powerless to achieve a satisfying speed and accuracy. In this paper, we propose a novel algorithm based on deep learning to solve this problem. We first use Simple Linear Iterative Clustering super-pixel algorithm to segment the original tunnel data and then artificially annotate the images. And eventually establish a dataset called CLS-CRACK for training and testing the deep convolutional neural network classification model. We design our classification network on the basis of ResNet18 and finish the experiments with one NVIDIA GeForce GTX Titan X on a framework named caffe. Finally, results demonstrate that the new algorithm performs well in the validation set with the result of a 94.0% accuracy and an 83.0% recall and time cost for each original tunnel image is within 0.025s. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pixels

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P-Order L2-norm distance Twin Support Vector machine 4

P-Order L2-norm distance Twin Support Vector Machine

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4th IAPR Asian conference on pattern recognition (ACPR)

作者： Ma, Xu Ye, Qiaolin Liu, Yingan Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Jiangsu Peoples R China Nanjing Forestry Univ Nanjing 210037 Jiangsu Peoples R China

ISBN: (纸本)9781538633540

Traditional machine learning methods are always formulated by l(2) - distances, which are prone to outlier data and not robust against outliers. To develop a robust machine learning method, we propose a new TWSVM method measured by l(2,p) - norm distances. the formula for this new method is difficult to solve because it needs to solve non-smooth optimization problems. In response to this problem, we provide an iterative algorithm in this article. this algorithm is proved to be strictly convergent and computationally feasible. We have done a lot of experiments on many benchmark datasets. the results show that our proposed algorithm not only has high precision, but also has better robustness.

关键词： Support vector machines Robustness machine learning Standards Clustering algorithms Iterative methods machine learning algorithms

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Face recognition using interest points and ensemble of classifiers

Face recognition using interest points and ensemble of class...

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international conference on Recent Advances in Information Technology (RAIT)

作者： A. Vinay Pratik Rajesh Sampat Sagar V. Belavadi R. Pratik B. S. Nikitha Rao Rahul Ragesh K. N. Balasubramanya Murthy S. Natarajan Centre for Pattern Recognition and Machine Intelligence PES University Bangalore Karnataka India

In human beings, it is the responsibility of the temporal lobe of the brain for recognition of faces. Certain features of the face trigger the neurons of the temporal lobe which are then stored. these eventually lead to the identification of the face. In machine learning system, a huge dataset of images is used as a reference by the face recognition algorithms. the most prominent features are extracted from what is referred to as landmarks on the face and are analyzed based on their relative position amongst other features. the main challenge in computer vision lies in the identification and extraction of these features which in turn assist in face recognition owing to the exponentially large degree of variability that can be seen in appearances. Speed and accuracy are some of the most prominent challenges for the same. We explore ELM critically and study its performance. Owing to its extremely fast learning, it finds potential in real-time applications. the goal of this paper is to evaluate the performance of the extreme learning machine algorithm for facial recognition when used as a standalone model as well as when it is used with an ensemble learner. We then conclude with a voting classifier which is responsible for the final predictions made. Various combinations of ensembles are explored to come up with the best performing model.

关键词： Feature extraction Face recognition Face Training Kernel Detectors Support vector machines

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Towards a Classification of Binary Similarity Measures 16th

Towards a Classification of Binary Similarity Measures

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16th Mexican international conference on Artificial Intelligence (MICAI)

作者： Ramirez Mejia, Ivan Batyrshin, Ildar Inst Politecn Nacl Ctr Invest Comp Av Juan de Dios BatizEsq Miguel Othon de Mendiza Ciudad De Mexico 07738 Mexico

ISBN: (纸本)9783030028374;9783030028367

Similarity measures for binary variables are used in many problems of machine learning, pattern recognition and classification. Currently, the dozens of similarity measures are introduced and the problem of comparative analysis of these measures appears. One of the methods used for such analysis is clustering of similarity measures based on correlation between data similarity values obtained by different measures. the paper proposes the method of comparative analysis of similarity measures based on the set theoretic representation of these measures and comparison of algebraic properties of these representations. the results show existing relationship between results of clustering and the classification of measures by their properties. Due to the results of clustering depend on the clustering method and on data used for measuring correlation between measures we conclude that the classification based on the proposed properties of similarity measures is more suitable for comparative analysis of similarity measures.

关键词： Similarity measure Binary data Contingency table Clustering

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Detection of negative emotion using acoustic cues and machine learning algorithms in Moroccan dialect 1

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9th international conference on Soft Computing and pattern recognition, SoCPaR 2017

作者： Agrima, Abdellah Elmazouzi, Laila Mounir, Ilham Farchi, Abdelmajid IMII Laboratory Faculty of Sciences and Technics University Hassan First Settat Morocco LAPSSII Laboratory Graduate School of Technology University Cadi Ayyad Safi Morocco

ISBN: (数字)9783319763576

ISBN: (纸本)9783319763569

the speech signal provides rich information about the speaker’s emotional state. therefore, recognition of the emotion in speech has become one of the research themes in the processing of speech and applications based on human-computer interaction. this article provides an experimental study and examines the detection of negative emotions such as fear and anger with regard to the neutral emotional state. the data set is collected from speeches recorded in the Moroccan Arabic dialect. Our aim is first to study the effects of emotion on the selected acoustic characteristics, namely the first four formants F1, F2, F3, F4, the fundamental frequency F0, Intensity, Number of pulses, Jitter and Shimmer and then compare our results to previous works. We also study the influence of phonemes and speaker gender on the relevance of these characteristics in the detection of emotion. To this aim, we performed classification tests using the WEKA software. We found that F0, Intensity, Number of Pulses have the best rates of recognition regardless speaker gender and phonemes. Moreover the second and third formant are the features that highlighted phoneme’s effect. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Natural frequencies

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Automatic Rail Flaw Localization and recognition by Featureless Ultrasound Signal Analysis 1

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14th international conference on machine learning and data Mining in pattern recognition, MLDM 2018

作者： Sulimova, Valentina Zhukov, Alexander Krasotkina, Olga Mottl, Vadim Markov, Anatoly Tula State University 92 Lenin Ave. Tula300012 Russia Sberbank of Russia 19 Vavilova St. Moscow117997 Russia Markov Processes International 475 Springfield Ave Suite 401 SummitNJ07901 United States Computing Center of the Russian Academy of Sciences Vavilov St. 40 Moscow119333 Russia Radioavionica Corporation Troitskiy pr. 4 building B Saint-Petersburg190005 Russia

ISBN: (数字)9783319961361

ISBN: (纸本)9783319961354

Ultrasound testing is a popular technique to find some hidden rail damages. In this paper we focus on the modern Russian railway flaw detectors, such as AVICON-14, which produce the results of ultrasound testing in the form of B-scan signals. We propose an approach simple enough to do fast automatic localization of B-scan signal segments, which could contain rail flaws. In order to recognize the selected segments as flaws of some kind or not flaws we apply SVM classifier jointly with DTW-based dissimilarity measure, specifically adapted by us to B-scan signals. To improve rail flaw localization and recognition quality we preprocess B-scan signals by applying some filter and making their convergence. Fast localization procedure jointly with CUDA implementation of B-scan segments comparison possesses the possibility to process big amounts of data. the experiments have shown that all rail flaws have been localized correctly and cross-validation ROC-scoreÂ =Â 0.82 for the rail flaw recognition has been reached. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Railroads

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