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检索条件"主题词=Learning algorithms"

共 13164 条记录，以下是4121-4130 订阅

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Evaluating a Logistic K-mer Based Model for Classifying CO1 Sequences of C. Clupeaformis

Evaluating a Logistic K-mer Based Model for Classifying CO1 ...

引用

International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019

作者： Jean, D. St Kunze, Herb Gillis, D. University of Guelph 50 Stone Rd. E Guelph Canada

ISBN: (纸本)9783030635909

Lake whitefish (Coregonus clupeaformis) are a primary support for subsistence and commercial fishing in Canada. In the 20th century, lake whitefish populations experienced a dramatic decline as a result of overfishing, environmental degradation, and predation. With proper environmental management and fishery management, populations have recovered, however certain local populations are still at risk. To properly manage these fisheries for sustainable yield, it is important that the genetic diversity of the population is maintained to ensure evolutionary potential of the species. The expensive technique of physically sampling populations is being replaced by sampling environmental DNA (eDNA) from the physical environment. However, the process of labelling eDNA sequences on a species level is still being developed. We found that techniques and theories from the well-established field of natural language processing, combined with machine learning algorithms, were extremely well-suited to labelling eDNA sequences. We built a logistic k-mer based learning model, inspired from natural language processing, which had high levels of classification accuracy. We anticipate this model is a starting point for more sophisticated machine learning models, and we have demonstrated how processes from the field of natural language processing are analogous to our eDNA process. © 2021, Springer Nature Switzerland AG.

关键词： learning algorithms

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Efficient Covering of a Target Object Using a Prediction Technique 10th

Efficient Covering of a Target Object Using a Prediction Tec...

引用

10th International Conference on Frontier Computing, FC 2020

作者： Ahn, Byeongtae Jang, Seok-Woo Anyang University Anyang14028 Korea Republic of

ISBN: (纸本)9789811601149

In recent years, as the shooting of video and the spread and viewing of video over the Internet have increased exponentially, the damage caused by the exposure of personal information has occurred. In this paper, we propose a method that robustly extracts the target object exposed personal information from continuous input video and then blocks the image using the image blurring technique while quickly tracking the extracted object using a prediction algorithm. The proposed method first extracts the target object exposed the personal information region from the input video using a neural network-based learning algorithm. The detected object is then blocked by applying image blurring while being quickly tracked using a position prediction algorithm. Experimental results show that the proposed method effectively protects personal information by quickly tracking and blurring the target object exposed personal information from the received video. The proposed method is expected to be useful for many practical applications such as privacy protection, video surveillance, object detection and tracking. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Methodology for Construction of Multi-approach Based Digital Twin in Strategic Communication 8

Methodology for Construction of Multi-approach Based Digital...

引用

8th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering, AIEEE 2020

作者： Zake, Mairita Majore, Ginta Krumins, Gatis Zakis, Zigurds Vidzeme University of Applied Sciences Faculty of Engineering Valmiera Latvia Vidzeme University of Applied Sciences Institute of Social Economic and Humanities Research Valmiera Latvia

ISBN: (纸本)9781665425384

In the last decade there is numerous in in demand of simulation modelling for various use cases and especially during Covid-19 crisis. With development of modern ICT and machine learning algorithms there is also opportunities for more comprehensive analysis and simulation modelling. Internet of Things (IoT) and machine learning algorithms as well as Big Data and computing technologies define background and prerequisites for development of digital twin as phenomena. Research includes analysis of application of existing methodologies for development of digital twin in strategic communication. This paper proposes also new methodology for development and evaluation process which is incorporation of already scientifically proved methods and next step forward for more unified process in simulation development. © 2021 IEEE.

关键词： learning algorithms

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LINEAR LAST-ITERATE CONVERGENCE IN CONSTRAINED SADDLE-POINT OPTIMIZATION 9

LINEAR LAST-ITERATE CONVERGENCE IN CONSTRAINED SADDLE-POINT ...

引用

9th International Conference on learning Representations, ICLR 2021

作者： Wei, Chen-Yu Lee, Chung-Wei Zhang, Mengxiao Luo, Haipeng University of Southern California United States

Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weights Update (OMWU) for saddle-point optimization have received growing attention due to their favorable last-iterate convergence. However, their behaviors for simple bilinear games over the probability simplex are still not fully understood - previous analysis lacks explicit convergence rates, only applies to an exponentially small learning rate, or requires additional assumptions such as the uniqueness of the optimal solution. In this work, we significantly expand the understanding of last-iterate convergence for OGDA and OMWU in the constrained setting. Specifically, for OMWU in bilinear games over the simplex, we show that when the equilibrium is unique, linear last-iterate convergence is achieved with a learning rate whose value is set to a universal constant, improving the result of (Daskalakis & Panageas, 2019b) under the same assumption. We then significantly extend the results to more general objectives and feasible sets for the projected OGDA algorithm, by introducing a sufficient condition under which OGDA exhibits concrete last-iterate convergence rates with a constant learning rate whose value only depends on the smoothness of the objective function. We show that bilinear games over any polytope satisfy this condition and OGDA converges exponentially fast even without the unique equilibrium assumption. Our condition also holds for strongly-convex-strongly-concave functions, recovering the result of (Hsieh et al., 2019). Finally, we provide experimental results to further support our theory. © 2021 ICLR 2021 - 9th International Conference on learning Representations. All rights reserved.

关键词： learning algorithms

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Corpus based classification of text in Australian contracts 8

Corpus based classification of text in Australian contracts

引用

8th Australasian Language Technology Association Workshop, ALTA 2010

作者： Curtotti, Michael McCreath, Eric Australian National University Canberra Australia

Written contracts are a fundamental framework for commercial and cooperative transactions and relationships. Limited research has been published on the application of machine learning and natural language processing (NLP) to contracts. In this paper we report the classification of components of contract texts using machine learning and hand-coded methods. Authors studying a range of domains have found that combining machine learning and rule based approaches increases accuracy of machine learning. We find similar results which suggest the utility of considering leveraging hand coded classification rules for machine learning. We attained an average accuracy of 83.48% on a multi-class labelling task on 20 contracts combining machine learning and rule based approaches, increasing performance over machine learning alone. © 2010 Australasian Language Technology Association Workshop 2010, ALTA 2010 - Proceedings. All rights reserved.

关键词： learning algorithms

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Free-space optical communication systems for B5G/6G networks 26

Free-space optical communication systems for B5G/6G networks

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26th Optoelectronics and Communications Conference, OECC 2021

作者： Bekkali, Abdelmoula Fujita, Hideo Hattori, Michikazu TOYO Electric Corporation Japan

This paper gives an overview of the recent trends of FSO systems for B5G/6G era, covering transceiver design, system architectures and integration with mmWave/THz links. The potential implementation of machine learnin... 详细信息

ISBN: (数字)9781943580927

ISBN: (纸本)9781557528209

关键词： learning algorithms

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Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags 1

Sentiment Classification of Code-Mixed Tweets using Bi-Direc...

引用

1st Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2021 at 16th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2021

作者： Mahata, Sainik Kumar Das, Dipankar Bandyopadhyay, Sivaji Institute of Engineering and Management Kolkata India Jadavpur University Kolkata India

ISBN: (纸本)9781954085060

Sentiment analysis tools and models have been developed extensively throughout the years, for European languages. In contrast, similar tools for Indian Languages are scarce. This is because, state-of-the-art pre-processing tools like POS tagger, shallow parsers, etc., are not readily available for Indian languages. Although, such working tools for Indian languages, like Hindi and Bengali, that are spoken by the majority of the population, are available, finding the same for less spoken languages like, Tamil, Telugu, and Malayalam, is difficult. Moreover, due to the advent of social media, the multi-lingual population of India, who are comfortable with both English ad their regional language, prefer to communicate by mixing both languages. This gives rise to massive code-mixed content and automatically annotating them with their respective sentiment labels becomes a challenging task. In this work, we take up a similar challenge of developing a sentiment analysis model that can work with English-Tamil code-mixed data. The proposed work tries to solve this by using bi-directional LSTMs along with language tagging. Other traditional methods, based on classical machine learning algorithms have also been discussed in the literature, and they also act as the baseline systems to which we will compare our Neural Network based model. The performance of the developed algorithm, based on Neural Network architecture, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively. ©2021 Association for Computational Linguistics

关键词： learning algorithms

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Non-intrusive load monitoring method based on LMD and KNN algorithm

Non-intrusive load monitoring method based on LMD and KNN al...

引用

2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Pengfei, Yang Xianbo, Sun Li, Zhu Yuhan, Wu Zhihao, Wang Hubei Minzu University School of Information Engineering Enshi China

ISBN: (纸本)9781665441285

Non-intrusive load monitoring technology is one of the key technologies for the construction of ubiquitous power Internet of Things, and load identification is the core content of non-intrusive load monitoring technology. When non-invasive load monitoring technology uses low dimensional feature data, the identification accuracy of similar loads is low. This paper designs a local mean decomposition algorithm(LMD) to decompose the low dimensional load features, and uses the load identification model established by k-nearest neighbor algorithm (KNN) to realize the identification of similar feature loads under low dimensional features. After non-intrusive load monitoring public data set simulation verification, the load identification model combined with LMD and KNN can realize accurate identification of similar loads. © 2021 IEEE.

关键词： learning algorithms

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Electric Intelligent Safety Monitoring Identification Based on YOLOv4

Electric Intelligent Safety Monitoring Identification Based ...

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2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2021

作者： Xiaolu, Cheng Chao, Gao Xiaoqi, Ye Rong, Si Xin, Song Hao, Ren Shenzhen Southern Power Grid Technology Development Co. Ltd. China Southern Power Grid Shenzhen China Shenzhen Power Supply Bureau Co. Ltd. China Southern Power Grid Shenzhen China Shenzhen Baoan Power Supply Bureau China Southern Power Grid Shenzhen China

ISBN: (纸本)9781665441285

With the continuous development of smart grid, the intelligent and automation level of power grid production safety supervision management mode needs to be improved. In order to solve this problem, this paper proposes a method based on YOLOv4 to identify the unsafe behaviors related to construction, including wearing safety helmet, working clothes and insulating gloves. In this paper, based on image recognition technology, the behavior analysis model is established to automatically identify whether the operator's behavior meets the safety requirements. The mAP of the final algorithm is 91.38%. In this paper, the judgment basis and logic of safety supervision alarm logic for multi construction scenarios are constructed, which provides a new idea for the application of deep learning algorithm in power intelligent safety supervision. © 2021 IEEE.

关键词： learning algorithms

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An Empirical Study of Neural Network Hyperparameters 8th

An Empirical Study of Neural Network Hyperparameters

引用

8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Makwe, Aditya Rathore, Abhishek Singh Institute of Engineering and Technology DAVV Indore India Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

ISBN: (纸本)9789811557873

The learning algorithms related to deep learning involves many attributes called hyperparameters, these variables help in determining the network structure. The performance of algorithms depends upon these hyper-parameter variables that are needed to be set prior to the actual implementation of the algorithm. This study involves an overview of some of the commonly used hyperparameters in the context of learning algorithms used for training neural networks along with the analysis of adaptive learning algorithms used for tuning learning rates. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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