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检索条件"主题词=Learning algorithms"

共 13140 条记录，以下是4181-4190 订阅

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Application of Deep learning Algorithm in Generator Fault Prediction 3

Application of Deep Learning Algorithm in Generator Fault Pr...

引用

3rd IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2020

作者： Yun, Xia Wu, Haiwei Information Center of Zhejiang University Hangzhou China Nanjing China

ISBN: (纸本)9781728198279

Recent rapid development of information and communication technology boosts the advance of distributed management and control system, especially for power system. Massive data and information have been accumulated, however, the meaningful fault information hidden in these data is not fully utilized, as the existing fault detection technologies are usually based on monitoring and diagnosis rather than prediction. In this paper, we introduce the deep learning algorithm into the fault prediction of generators in power system, and explore the validity and feasibility of generator operation data in fault prediction application. Our method includes two parts, the first is a Partial Least Square (PLS)-based pre-process module which is used to reduce the feature dimension, the second is a deep linear regression model which is dedicated to regressing the generator operation data and predicting the fault behavior of generators. Experimental results demonstrate the effectiveness of our method. © 2020 IEEE.

关键词： learning algorithms

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Reinforcement learning dynamics in the infinite memory limit 19

Reinforcement learning dynamics in the infinite memory limit

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19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

作者： Barfuss, Wolfram School of Mathematics University of Leeds United Kingdom Max Planck Institute for Mathematics in the Sciences Leipzig Germany

ISBN: (纸本)9781450375184

Reinforcement learning algorithms have been shown to converge to the classic replicator dynamics of evolutionary game theory, which describe the evolutionary process in the limit of an infinite population. However, it is not clear how to interpret these dynamics from the perspective of a learning agent. In this paper we propose a data-inefficient batch-learning algorithm for temporal difference Q learning and show that it converges to a recently proposed deterministic limit of temporal difference reinforcement learning. In a second step, we state a data-efficient learning algorithm, that uses a form of experience replay, and show that it retains core features of the batch learning algorithm. Thus, we propose an agent-interpretation for the learning dynamics: What is the infinite population limit of evolutionary dynamics is the infinite memory limit of learning dynamics. © 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

关键词： learning algorithms

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Sentimental Analysis based on hybrid approach of Latent Dirichlet Allocation and Machine learning for Large-Scale of Imbalanced Twitter Data 3

Sentimental Analysis based on hybrid approach of Latent Diri...

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3rd International Conference on algorithms, Computing and Artificial Intelligence, ACAI 2020

作者： Jamal, Nasir Xianqiao, Chen Hussain Abro, Junaid Tukhtakhunov, Doniyor School of Computer Science and Technology Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781450388115

Emotions classification in large amount of Twitter's data is very effective to analyze the users' mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract meaningful features from a burst of raw tweets as emotions are subjective with limited fuzzy boundaries. These subjective features can be expressed in different terminologies and perceptions. In this paper, we proposed a hybrid approach of LDA and machine learning to predict emotions for large scale of imbalanced tweets. First, the raw tweets are preprocessed using tokenization method for capturing useful features without noisy information. Second, the local and global feature's importance is estimated by applying TFIDF statistical technique. Third, the Latent Dirichlet Allocation (LDA) topic modeling method is used to extract topics from these features. These topics explain concepts of related tweet which is really helpful for classification. Fourth, the Adaptive Synthetic (ADASYN) class balancing technique is applied to oversample the data and balance each class of topic. Finally, the K-Nearest Neighbor (KNN) machine learning algorithm is applied to predict the emotions in extracted topics. The class balancing method increase the significance of minor classes and solve the problem of class imbalance. The proposed approach is evaluated on two different Twitters' emotions datasets. It is proved that, this methodology outperformed as compared to the popular state of the art methods in terms of precision, recall, f-measure and classification accuracy. © 2020 ACM.

关键词： learning algorithms

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A Secure Algorithm for Deep learning Training under GAN Attacks

A Secure Algorithm for Deep Learning Training under GAN Atta...

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2020 IEEE International Conference on Communications, Computing, Cybersecurity, and Informatics, CCCI 2020

作者： Prashar, Aseem Salinas Monroy, Sergio A. Wichita State University Department of Electrical Engineering and Computer Science Wichita United States

ISBN: (纸本)9781728120355

Deep neural networks have outperformed traditional machine learning approaches for many tasks, and are the tool of choice in many fields. However, directly applying these techniques in fields that deal with private data is challenging. The reason is that a third-party, which organizations may not trust, usually needs to centrally collect private data. To overcome this challenge, researches have proposed distributed training algorithms that allow multiple users to collaboratively train their local deep learning models without sharing private datasets. However, these approaches are vulnerable to recently proposed attacks where a malicious user can replicate private data from another user by compromising the collaborative training algorithm. In this paper, we propose a privacy-preserving distributed deep learning algorithm that allows a user to leverage the private datasets from a group of users while protecting its privacy. Our algorithm prohibits this user from ever sharing the parameters of its model, and thus it prevents malicious users from compromising the training and replicating the user's private data. We conduct extensive experiments and observe that our algorithm can achieve a model accuracy of 95.18 %, which is the same accuracy that previous approaches that are vulnerable to attacks can achieve. © 2020 IEEE.

关键词： learning algorithms

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Reinforcement learning of active learning strategies: An evaluation 20

Reinforcement learning of active learning strategies: An eva...

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20e Extraction et Gestion des Connaissances, EGC 2020 - 20th Conference on Knowledge Extraction and Management, EGC 2020

作者： Desreumaux, Louis Lemaire, Vincent Université de Technologie de Compiègne France Orange Labs

ISBN: (纸本)9791096289127

Active learning aims to reduce annotation cost by predicting which samples are useful for a human expert to label. In the literature, most selection strategies are hand-designed, and it has become clear that there is no best active learning strategy. This has motivated research into meta-learning algorithms for "learning how to actively learn" (Konyushkova et al., 2019). In this paper, we compare this approach with margin sampling, reported in recent comparative studies as a very competitive heuristic (Yang et Loog, 2018;Pereira-Santos et al., 2019). © 2020 Extraction et Gestion des Connaissances, EGC 2020. All rights reserved.

关键词： learning algorithms

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Reliability Evaluation Methods of Deep learning Algorithm in Computer Vision 20

Reliability Evaluation Methods of Deep Learning Algorithm in...

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2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2020

作者： Xu, Xin Gao, Yan China Electronic Products Reliability and Environmental Testing Research Institute Guangzhou China

ISBN: (纸本)9781450388306

With the rapid development of deep learning technology, the application based on deep learning shows explosive growth, especially in the field of computer vision. However, some characteristics of the deep learning algorithm make it face many unexpected threats in actual use. It has become an urgent problem to ensure the reliability of the algorithm. In this paper, we propose a deep learning algorithm reliability evaluation system and corresponding evaluation indicators from the three aspects of data, model, and operating framework, then describe reliability evaluation methods for each specific indicator. © 2020 ACM.

关键词： learning algorithms

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Few is enough: Task-augmented active meta-learning for brain cell classification 23rd

Few is enough: Task-augmented active meta-learning for brain...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Yuan, Pengyu Mobiny, Aryan Jahanipour, Jahandar Li, Xiaoyang Cicalese, Pietro Antonio Roysam, Badrinath Patel, Vishal M. Dragan, Maric Van Nguyen, Hien University of Houston HoustonTX United States National Institutes of Health Bethesda MD United States Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783030597092

Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant cost. Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process. In this paper, we propose a tAsk-auGmented actIve meta-learning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples. AGILE combines a meta-learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. It then uses Bayesian dropout uncertainty estimates to actively select the most difficult samples when updating the model to a new task. This allows AGILE to learn with fewer tasks and a few informative samples, achieving high performance with a limited dataset. We perform our experiments using the brain cell classification task and compare the results to a plain meta-learning model trained from scratch. We show that the proposed task-augmented meta-learning framework can learn to classify new cell types after a single gradient step with a limited number of training samples. We show that active learning with Bayesian uncertainty can further improve the performance when the number of training samples is extremely small. Using only 1% of the training data and a single update step, we achieved 90% accuracy on the new cell type classification task, a 50% points improvement over a state-of-the-art meta-learning algorithm. © Springer Nature Switzerland AG 2020.

关键词： learning algorithms

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Multi-class Image Classification Using Deep learning Algorithm 4

Multi-class Image Classification Using Deep Learning Algorit...

引用

4th International Conference on Advanced Technology and Applied Sciences, ICaTAS 2019

作者： Ezat, W.A. Dessouky, M.M. Ismail, N.A. Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Menoufia Egypt

Classifying images is a complex problem in the field of computer vision. The deep learning algorithm is a computerized model simulates the human brain functions and operations. Training the deep learning model is a costly process in machine resources and time. Investigating the performance of the deep learning algorithm is mostly needed. The convolutional neural network (CNN) is most commonly used to build a structure of the deep learning models. In this paper convolutional neural network (CNN) model pre-trained on Image-Net is used for classification of images of the PASCAL VOC 2007 data-set. The transfer learning approach is used to improve the performance of the deep learning CNN model where classification works fairly well with the smallest amount of computation time and fewer machine resources. The behavior of the Deep learning CNN model is studied and the performance has been measured. The obtained results are compared with the obtained test results from the Super-vector coding of local image descriptors method, SVM method, and Region Ranking SVM method, which tested with the PASCAL VOC 2007 data-set. The final results evaluate the deep learning algorithm as a state-of-the-art method for an image classification task. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

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Multi-factor Stock Selecting Model Based on Residual Net and LSTM Deep learning Algorithm

Multi-factor Stock Selecting Model Based on Residual Net and...

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2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020

作者： Chen, Muhao Guo, Weixia Illinois Institute of Technology Faculty of Education ChicagoIL60616-3793 United States

ISBN: (纸本)9781728165202

This paper builds a multi-factor stock selection model based on optimized deep learning algorithms, using SFS, GA and XGBoost for factor combination and feature selection. Besides, applying Multi-LR to integrate Residual Net and LSTM to predict the stock's weekly change, which better simulates the randomness, information asymmetry and timing of financial markets. The quantitative models in this paper have excellent prediction accuracy, return and stability. © 2020 IEEE.

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Scratch Detection of the PET Bottle Preform Based on Deep learning 2

Scratch Detection of the PET Bottle Preform Based on Deep Le...

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2nd International Conference on Information Technology and Computer Application, ITCA 2020

作者： Zhou, Lei Liang, Jianan Cao, Yongjun Institute of Intelligent Manufacturing Gdas Guangdong Key Laboratory of Modern Control Technology Guangzhou China South China Robotics Innovation Research Institute Motion Control and Equipment Center Foshan China

ISBN: (纸本)9780738111414

Scratches are a common phenomenon in the production of the PET bottle preform, and traditional inspection by human eyes bring troubles to the automatic production process. In this paper, deep learning algorithm was used to detect PET bottle preform scratches. The detection algorithm based on YOLOv4 neural network was proposed. The detection effect was tested under different scratch degrees, which provide accurate information for automatic production picking of the PET bottle preform. The average detection time of a single image was 0.154s, and the shortest detection time was 0.133s. © 2020 IEEE.

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