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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022"

共 265 条记录，以下是31-40 订阅

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Research and application of a hierarchical fault diagnosis system based on support vector machine

Research and application of a hierarchical fault diagnosis s...

引用

3rd international conference on Natural Computation (ICNC 2007)

作者： Liu, Ailun Yuan, Xiaoyan Yu, Jinshou E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)0769528759

Support Vector machine (SVM) is a kind of machine learning method based on the statistical learning theory, it has been applied in the fault diagnosis field. After analyzing SVM pattern classification theory, a hierarchical structure Fault Detection and Identification (FDI) system is presented in this paper, and simulation results show that this method can effectively handle the complex process characteristic and improve FDI model performance.

关键词： support Vector machine (SVM) fault diagnosis pattern recognition

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Bangla Handwritten Basic Character recognition Using Deep Convolutional Neural Network 8

Bangla Handwritten Basic Character Recognition Using Deep Co...

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： Saha, Chandrika Faisal, Rahat Hossain Rahman, Md Mostafijur Univ Barishal Dept Comp Sci & Engn Barishal Bangladesh

ISBN: (纸本)9781728107882

Bangla is one of the mostly spoken languages all over the world, nonetheless, efforts on Bangla handwritten character recognition is not adequate. The use of Deep Convolutional Neural Network (DCNN) based classifiers has become a triumph over the state of art machine learning techniques. Using DCNN model to classify Bangla isolated basic characters can provide better output because of its ability to detect many hidden features from an image. In this paper, a new DCNN model, namely, BBCNet-15 is proposed for Bangla handwritten basic character recognition. the proposed model consists of 6 convolution layers, 6 max pooling layers, 2 fully connected layers followed by the softmax output unit. To avoid overfitting dropout regularization technique is used. The implementation of the proposed DCNN model is evaluated on a benchmark dataset, CMATErdb 3.1.2 which contains 50 character classes including 39 consonants and 11 vowels. The experimental evaluation of BBCNet-15 on the dataset provides a recognition accuracy of 96.40% which outperforms some prominent techniques in existence.

关键词： Deep learning Convolutional Neural Network pattern recognition Optical Character recognition Bangla Handwritten Character recognition

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Bangladeshi License Plate recognition Using Adaboost Classifier 8

Bangladeshi License Plate Recognition Using Adaboost Classif...

引用

作者： Dhar, Prashengit Abedin, Md Zainal Karim, Razuan Fatema-Tuj-Johora Hossain, Mohammad Shahadat Univ Sci & Technol Chittagong Dept Comp Sci & Engn Chattogram Bangladesh Univ Chittagong Dept Comp Sci & Engn Chittagong 4331 Bangladesh

ISBN: (纸本)9781728107882

License plate recognition (LPR) is a technology for the authentication of a vehicle by locating and recognizing the license plate number in an image through computer vision techniques and machine learning models. To develop intelligent traffic management such as vehicle monitoring, LPR is a key component. However, due to the diversity of layouts and characters of plates, universal solution is not possible. So, this research focuses on development of an algorithm for the recognition of license plate of Bangladesh by using image processing's and machine learning model. This algorithm executes in three steps: detection of the plate with shape verification, tilt correction and recognition of the number. For detection, RGB color space, median filtering, binarization, morphological analysis, region properties for filtering are applied. To discard noisy object, shape verification is done through robust distances to borders vectors. Before character segmentation, horizontal tilt correction is applied. Then, characters are extracted by using bounding box parameters from the extracted plate. Finally, the recognition is implemented by using the blending of Histogram Oriented Gradient (HOG) and Local Binary pattern (LBP) features and adaptive boosting (Adaboost) classifier is used to categorize the characters. The proposed algorithm is simulated on the images which are captured from different roads of Bangladesh. The experimental result shows that the detection and recognition accuracy is noteworthy.

关键词： License Plate recognition (LPR) RGB Color Model Distance to Borders (DtBs) Character Segmentation HOG (Histogram of Oriented Gradient) Local Binary pattern (LBP) Adaboost

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Naive Bayes classifier: True and estimated errors for 2-class, 2-features case

Naive Bayes classifier: True and estimated errors for 2-clas...

引用

3rd IEEE international conference on Intelligent Systems

作者： Hoare, Zoe Univ Wales Sch Informat Bangor LL57 1UT Gwynedd Wales

ISBN: (纸本)9781424401956

The low error rate of Naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided.

关键词： machine learning pattern recognition Naive Bayes error rates

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A game theory approach to pairwise classification with support vector machines

A game theory approach to pairwise classification with suppo...

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3rd international conference on machine learning and Applications

作者： Petrovskiy, M Moscow MV Lomonosov State Univ Dept Comp Sci MSU Moscow 119899 Russia

ISBN: (纸本)0780388232

Support Vector machines (SVM) for pattern recognition are discriminant binary classifiers. One of the approaches to extend them to multi-class case is pairwise classification. Pairwise comparisons for each pair of classes are combined together to predict the class or to estimate class probabilities. This paper presents a novel approach, which considers the pairwise SVM classification as a decision-making problem and involves game theory methods to solve it. We prove that in such formulation the solution in pure minimax strategies is equivalent to the solution given by standard fuzzy pairwise SVM method. On the other hand, if we use mixed strategies it,e formulate new linear programming based pairwise SVM method for estimating class probabilities. We evaluate the performance of the proposed method in experiments with several benchmark datasets, including datasets for optical character recognition and multi-class text categorization problems.

关键词： pattern recognition

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Shape analysis of hippocampus in temporal lobe epilepsy using Signed Poisson Mapping 3

Shape analysis of hippocampus in temporal lobe epilepsy usin...

引用

3rd international conference on pattern Analysis and Image Analysis (IPRIA)

作者： Farazi, Mohammad Soltanian-Zadeh, Hamid Univ Tehran Sch Elect & Comp Engn CIPCE Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Henry Ford Hlth Syst Dept Radiol Med Image Anal Lab Detroit MI USA

ISBN: (纸本)9781509064540

Complementary role of computer assisted models using machine learning methods in medical imaging has been a center of attention in recent years. Shape analysis of the brain structures can be used to evaluate their abnormalities and deformations, specifically in patients suffering from neurological diseases like epilepsy, Alzheimer, and Parkinson. We propose an automatic diagnosis and lateralization algorithm using Signed Poisson Mapping (SPoM), which has been recently proposed as a new framework for shape analysis of three-dimensional (3D) structures. In contrast to previous studies, we use a three-class classification to show the robustness of our algorithm in differentiating between normal, left temporal lobe epilepsy (LTLE), and right temporal lobe epilepsy (RTLE) subjects. We also use a support vector machine (SVM) classifier with a radial basic function (RBF) kernel for lateralization, i.e., differentiating between RTLE and LTLE patients. The classification accuracy for the three-class classifier is 94% and for the lateralization task is 95% which is superior to those reported in the related literature.

关键词： 3D shape analysis magnetic resonance imaging (MRI) SPoM lateralization temporal lobe epilepsy

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ANLAGIS: Adaptive Neuron-Like Network Based on learning Automata Theory and Granular Inference Systems with Applications to pattern recognition and machine learning

ANLAGIS: Adaptive Neuron-Like Network Based on Learning Auto...

引用

3rd international Work-conference on the Interplay Between Natural and Artificial Computation

作者： Maravall, Dario de Lope, Javier Univ Politecn Madrid E-28040 Madrid Spain

ISBN: (纸本)9783642022630

In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between pre-synaptic and post-synaptic learning in artificial neural networks. To illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. Previous work of Jang et al. [1] on adaptive network-based fuzzy inference systems, or simply ANFIS, can be considered a precursor of ANLAGIS. The main, novel contribution of ANLAGIS is the incorporation of learning Automata Theory within its structure.

关键词： Artificial Neural Networks Granular Computing learning Automata Theory pattern recognition machine learning

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Proceedings of the 3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

Proceedings of the 3rd International Conference on Smart Tec...

引用

3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

ISBN: (纸本)9781665456647

The proceedings contain 57 papers. The topics discussed include: an effective emotion recognition method using facial and speech features;machine learning based predictive analysis of diseases in health care;machine learning-based personalized recommendation system for e-learners;prediction of DDoS flooding attack using machine learning models;image based crack recognition on concrete structures using convolutionalal neural networks;a machine vision-based person detection under low-illuminance conditions using high dynamic range imagery for visual surveillance system;social media event summarization using neural networks;review of deep reinforcement learning-based recommender systems;a comprehensive analysis of deep learning frameworks to mitigate the impact of varied lighting and weather conditions;security attacks and key challenges in blockchain technology: a survey;interference aware routing in 5G backhaul wireless mesh networks;and cloud computing based workload optimization using long short term memory algorithm.

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learning multi-label alternating decision trees from texts and data

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3rd international conference on machine learning and Data Mining in pattern recognition

作者： De Comité, F Gilleron, R Tommasi, M Univ Lille 1 F-59655 Villeneuve Dascq France Univ Lille 3 F-59653 Villeneuve Dascq France

ISBN: (纸本)3540405046

Multi-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer's *** and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data.

关键词： boosting alternating decision trees text mining multi-label problems

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Persian Handwritten Digits recognition by Using Zoning and Histogram Projection

Persian Handwritten Digits Recognition by Using Zoning and H...

引用

3rd Joint conference of AI and Robotics / 5th RoboCup Iran Open international Symposium

作者： Nooraliei, Amir Islamic Azad Univ Qazvin Branch Dept Elect Comp & IT Engn Qazvin Iran

ISBN: (纸本)9781467363150

In this paper, Persian handwritten digits reorganization by using zoning features and projection histogram for extracting feature vectors with 69-dimensions is presented. In classification stage, support vector machines (SVM) with three linear kernels, polynomial kernel and Gaussian kernel have been used as classifier. We tested our algorithm on the dataset that contained 8600 samples of Persian handwritten digits for performance analysis. Using 8000 samples in learning stage and another 600 samples in testing stage. The results got with use of every three kernels of support vector machine and achieved maximum accuracy by using Gaussian kernel with gamma equal to 0.16. In pre-processing stage only image binarization is used and all the images of this dataset had been normalized at center with size 40x40. The recognition rate of this method, on the test dataset 97.83% and on all samples of dataset 100% was earned.

关键词： pattern recognition Optical character recognition Support vector machine Persian handwritten digits

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