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检索条件"主题词=Learning algorithms"

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Information-extreme machine learning on-board recognition system of ground objects with the adaptation of the input mathematical description 3

Information-extreme machine learning on-board recognition sy...

引用

3rd International Workshop on Computer Modeling and Intelligent Systems, CMIS 2020

作者： Dovbysh, Anatoliy Naumenko, Igor Myronenko, Mykyta Savchenko, Taras Sumy State University Rymskogo-Korsakova st. 2 Sumy40007 Ukraine Scientific-research center of missiletroopsand artillery Gerasim Kondratyev st 165 Sumy400021 Ukraine

The method of machine learning of the on-board unmanned aerial vehicle system for autonomous recognition of natural and infrastructural terrestrial objects with the optimization of the frame size generated by the optical digital image of the region is considered. The problem of information synthesis of the onboard recognition system is being solved in the framework of information-extreme intellectual data analysis technology. As part of the functional approach to modeling the cognitive processes inherent in man in the formation and making of classification decisions, the method of information-extreme machine learning onboard recognition system, which allows to adapt the input mathematical description to the maximum full probability of correct recognition of terrestrial features. In accordance with the proposed categorical model, an algorithm of information-extreme machine learning was developed and pro-grammatically implemented, during which the parameters of the sys-tem functioning according to the modified Kullback information criterion are optimized. Implementation of the proposed machine learning algorithm was carried out on the example of recognition of natural and infrastructural land objects, among which roads were considered as a zone of interest. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： learning algorithms

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Interpretable Machine learning Based on Integration of NLP and Psychology in Peer-to-Peer Lending Risk Evaluation 9th

Interpretable Machine Learning Based on Integration of NLP a...

引用

9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Li, Lei Zhao, Tianyuan Xie, Yang Feng, Yanjie Beijing University of Posts and Telecommunications Beijing100876 China Shanghai University of International Business and Economics Shanghai201620 China

ISBN: (纸本)9783030604561

With the rapid development of Peer-to-Peer (P2P) lending in the financial field, abundant data of lending agencies have appeared. P2P agencies also have problems such as absconded with ill-gotten gains and out of business. Therefore, it is urgent to use the interpretable AI in Fintech to evaluate the lending risk effectively. In this paper we use the machine learning and deep learning method to model and analyze the unstructured natural language text of P2P agencies, and we propose an interpretable machine learning method to evaluate the fraud risk of P2P agencies, which enhances the credibility of the AI model. First, this paper explains model behavior based on the psychological interpersonal fraud theory in the field of social science. At the same time, the NLP and influence function in the field of natural science are used to verify that the machine learning model really learns the information of part-of-speech details in the fraud theory, which provides the psychological interpretable support for the model of P2P risk evaluation. In addition, we propose "style vectors" to describe the overall differences between text styles of P2P agencies and understand model behavior. Experiments show that using style vectors and influence functions to describe text style differences is the same as human intuitive perception. This proves that the machine learning model indeed learn the text style difference and use it for risk evaluation, which further shows that the model has a certain machine learning interpretability. © 2020, Springer Nature Switzerland AG.

关键词： learning algorithms

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Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization 1

Seeing stars when there aren't many stars: Graph-based semi-...

引用

1st Workshop on Graph-Based algorithms for Natural Language Processing, Textgraphs 2006 at Human Language Technologies

作者： Goldberg, Andrew B. Zhu, Xiaojin Computer Sciences Department University of Wisconsin-Madison Madison WI53706 United States

We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., "4 stars"), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where labeled data is scarce. We place this task in the semi-supervised setting and demonstrate that considering unlabeled reviews in the learning process can improve rating-inference performance. We do so by creating a graph on both labeled and unlabeled data to encode certain assumptions for this task. We then solve an optimization problem to obtain a smooth rating function over the whole graph. When only limited labeled data is available, this method achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training. © 2006 Association for Computational Linguistics

关键词： learning algorithms

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Research and Application of Process Object Modeling Method Based on Deep learning 4th

Research and Application of Process Object Modeling Method B...

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4th International Conference on Intelligent, Interactive Systems and Applications, IISA2019

作者： Yin, Feng Ye, Su Li, Quan Sun, Jiandong Cai, Junyu Institute of Electric Power Science State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

ISBN: (纸本)9783030343866

Predictive control is often used to improve control performance because most industrial sites are inertial lag objects, but it is difficult to establish predictive model. In this paper, artificial intelligence method is applied to control object modeling. A deep learning based modeling method is proposed, which includes open-loop modeling and closed-loop modeling. Closed-loop modeling is based on open-loop modeling. Firstly, the DNN deep learning algorithm used in this paper is introduced. Aiming at open-loop data of multi-order inertial system, a deep learning DNN network system based on Multiple Inertial filters is designed. After training step disturbance data, a deep learning neural network model with object characteristics is obtained. Secondly, a closed-loop modeling method based on two DNN models is proposed for multi-order inertial closed-loop systems. Firstly, forward and backward step disturbances are added to the control variables, then model 1 is obtained after training the DNN network system with deep learning based on Multiple Inertial filters, and then forward and backward step disturbances are added to the set values. The output data of model 1 and the output data of controlled objects are trained as inputs of deep learning DNN random inactivation network, and the closed-loop object model is finally obtained. © 2020, Springer Nature Switzerland AG.

关键词： learning algorithms

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learning from few positives: A provably accurate metric learning algorithm to deal with imbalanced data 29

Learning from few positives: A provably accurate metric lear...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Viola, Rémi Emonet, Rémi Habrard, Amaury Metzler, Guillaume Sebban, Marc University of Lyon UJM-Saint-Etienne CNRS Institute of Optics Graduate School Laboratoire Hubert Curien UMR 5516 Saint-Etienne France DGFiP Ministry of Economy and Finances Paris France

ISBN: (纸本)9780999241165

learning from imbalanced data, where the positive examples are very scarce, remains a challenging task from both a theoretical and algorithmic perspective. In this paper, we address this problem using a metric learning strategy. Unlike the state-of-the-art methods, our algorithm MLFP, for Metric learning from Few Positives, learns a new representation that is used only when a test query is compared to a minority training example. From a geometric perspective, it artificially brings positive examples closer to the query without changing the distances to the negative (majority class) data. This strategy allows us to expand the decision boundaries around the positives, yielding a better F-Measure, a criterion which is suited to deal with imbalanced scenarios. Beyond the algorithmic contribution provided by MLFP, our paper presents generalization guarantees on the false positive and false negative rates. Extensive experiments conducted on several imbalanced datasets show the effectiveness of our method. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： learning algorithms

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Review of visible watermark's influence on private information-related machine learning techniques 2

Review of visible watermark's influence on private informati...

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2019 2nd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2019

作者： Lyu, Yizhe Shenyuan Honors College Beihang University Beijing100191 China

Under the increasing needs of sharing datasets and risks of leaking privacy during data sharing, there is growing attention of the owners of datasets to claim ownership and protect their privacy from being misused. To achieve this secure purpose, the visible watermark may be an effective way. However, due to the loss of visual information, it is still unclear the influence of these visible watermark methods on related machine learning techniques. To investigate these, this paper provides a review of visible watermark's influence on private information-related machine learning techniques. This paper compared the performance of different CNN methods with or without visible watermarking. According to the results of the experiments, the accuracy of watermarked images' identification decreased significantly. By average, the accuracy would decrease by 4.75%. And the performance changed when using different CNN structures, watermarked methods and datasets. After that, there are some experiments to explore the feasibility of using none-watermarked images trained network to identify the watermarked images. The results show that the accuracy of identification at least decreased by 25% when only one of the train set and test set are watermarked. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

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learning to reason in round-based games: Multi-task sequence generation for purchasing decision making in first-person shooters 16

Learning to reason in round-based games: Multi-task sequence...

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16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2020

作者： Zeng, Yilei Lei, Deren Li, Beichen Jiang, Gangrong Ferrara, Emilio Zyda, Michael University of Southern California United States

ISBN: (纸本)9781577358497

Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. CounterStrike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, provides an excellent environment for multi-player round-based sequential reasoning. In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions. We adopt few-shot learning to sample multiple rounds in a match, and modified model agnostic meta-learning algorithm Reptile for the meta-learning loop. We formulate each round as a multi-task sequence generation problem. Our state representations combine action encoder, team encoder, player features, round attribute encoder, and economy encoders to help our agent learn to reason under this specific multi-player round-based scenario. A complete ablation study and comparison with the greedy approach certify the effectiveness of our model. Our research will open doors for interpretable AI for understanding episodic and long-term purchasing strategies beyond the gaming community.1 Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： learning algorithms

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Deep learning Techniques for Automatic Modulation Classification: A Systematic Literature Review 14

Deep Learning Techniques for Automatic Modulation Classifica...

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14th International Conference on Innovations in Information Technology, IIT 2020

作者： Ghunaim, Sara Ali Nasir, Qassim Talib, Manar Abu University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9781728181844

Implementation of Automatic modulation classification originated in the military field several years ago. Threat analysis, surveillance, and welfare where of great concern, thus this urged the importance to study the application of automatic modulation classification for signal recognition. In this research survey a Systematic Literature Review is conducted to study the implementation of deep learning algorithms in modulation classification. Our survey conducts a study from four perspectives including: deep learning techniques/models, performance metrics used, deep learning models adopted, and types of modulation classification classified. © 2020 IEEE.

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Simulation of learning Logical Functions Using Single-Layer Perceptron 1

引用

International Conference on Recent Advancement in Computer, Communication and Computational Sciences, RACCCS 2019

作者： Ahamad, Mohd Vasim Ali, Rashid Naz, Falak Fatima, Sabih Department of Computer Engineering ZHCET Aligarh Muslim University Aligarh India Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh India

ISBN: (数字)9789811515187

ISBN: (纸本)9789811515170

As a simplest neural network, the perceptron computes a linear combination of real-valued labeled training samples and predicts the classes for unclassified testing samples. If two different sets of samples can be separated by a straight line, they are called linearly separable. The perceptron can be considered as the binary classifier of unclassified samples based on the supervised machine learning approach. The learning algorithm for the perceptron takes a static value for the learning rate as an input, which can affect the efficiency of the learning process. This work attempts to implement logical operations OR, AND, NOR, and NAND using a single-layer perceptron algorithm. A modified perceptron algorithm is proposed which finds the most optimal value of learning for the learning process. The learning algorithm is provided with a range of learning rate values, and it picks the most suitable learning rate by calculating the number of iterations taken by each of these learning rates and choosing the one which takes the minimum number of the epoch. © 2020, Springer Nature Singapore Pte Ltd.

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Distributed Control Strategy of DC Link based on Q-learning for Distribution Network Operation

Distributed Control Strategy of DC Link based on Q-Learning ...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Qi, Qi Jiang, Qirong Tsinghua University Department of Electrical Engineering Beijing China

ISBN: (纸本)9781728155081

With the integration of renewable generations and the increase of electrical demands, distribution networks are facing challenges including excessive power losses and voltage violations. This paper investigates the utilization a DC link to increase the operational flexibility of a distribution network. Considering the strong dependence on communication infrastructures and the complex optimization process of centralized control schemes, a distributed control strategy of DC link is proposed to provide continuous power flow control and voltage regulation with limited network information. In this strategy, Q-learning algorithm, which belongs to the category of machine learning methods, is used to learn interactions between a DC link and distribution network, and to adjust the active power outputs of the DC link online. The reactive power outputs at each terminal of the DC link are tuned optimally using local voltage information. Effectiveness of the proposed control strategy of DC link is verified on IEEE 69-bus distribution system. Results show that the proposed control strategy is able to reduce power losses and improve voltage profiles simultaneously with limited measurement from a distribution network. Compared to centralized controls, the proposed control strategy achieves near-optimal performances with a moderate computational burden. © 2020 IEEE.

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