A model-free Bayesian classifier

Geng, Zhiqiang; Meng, Qingchao; Bai, Ju; Chen, Jie; Han, Yongming; Wei, Qin; Ouyang, Zhenhua

doi:10.1016/j.ins.2019.01.026

Cited by 24 publications

(11 citation statements)

References 35 publications

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“…(3) The AKRL model proposed in this paper can be used not only for relation prediction tasks, but also for relation classification. In the future, we can further study the independence between attributes and relations, and try to complete the task of relation classification by combining Naive Bayesian classifier or model-free Bayesian classifier [20] and other machine learning methods.…”

Section: Discussionmentioning

confidence: 99%

Representation Learning of Knowledge Graphs With Entity Attributes

et al. 2020

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Most of the existing knowledge representation learning methods project the entities and relations represented by symbols in the knowledge graph into the low-dimensional vector space from the perspective of the structure and semantics of triples, and express the complex relations between entities and relations with dense low-dimensional vectors. However, triples in the knowledge graph not only contain relation triples, but also contain a large number of attribute triples. Existing knowledge representation methods often confuse these two kinds of triples and pay little attention to the semantic information contained in attributes and attribute values. In this paper, a novel representation learning method which makes use of the attribute information of entities is proposed. Specifically, deep convolutional neural network model is used to encode attribute information of entities, and both attribute information and triple structure information are utilized to learn knowledge representation, and then generate attribute-based representation of entities. The knowledge graph completion task was used to evaluate this method, and the experimental results on open data sets FB15K and FB24k showed that the attribute-embodied knowledge representation learning model outperforms the other baselines.INDEX TERMS Attribute, knowledge graph, representation learning.

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Section: Discussionmentioning

confidence: 99%

Representation Learning of Knowledge Graphs With Entity Attributes

et al. 2020

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“…In both cases, the idea of functioning is almost identical and is based on classification using a group of decision trees; the biggest difference in the construction of both algorithms is the so-called bootstrap. The simple Bayesian classifier is an uncomplicated, probabilistic classifier used in machine learning methods to solve the problems of sorting and classification [36,37]. The task of the Bayes classifier is to assign a new case to one of the decision classes, while the set of decision classes must be finite and defined a priori.…”

Section: Methods and Techniques For Generating Workplace Proceduresmentioning

confidence: 99%

A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

Patalas‐Maliszewska

Halikowski

2019

Symmetry

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(1) Background: Improving the management and effectiveness of employees’ learning processes within manufacturing companies has attracted a high level of attention in recent years, especially within the context of Industry 4.0. Convolutional Neural Networks with a Support Vector Machine (CNN-SVM) can be applied in this business field, in order to generate workplace procedures. To overcome the problem of usefully acquiring and sharing specialist knowledge, we use CNN-SVM to examine features from video material concerning each work activity for further comparison with the instruction picture’s features. (2) Methods: This paper uses literature studies and a selected workplace procedure: repairing a solid and using a fuel boiler as the benchmark dataset, which contains 20 s of training and a test video, in order to provide a reference model of features for a workplace procedure. In this model, the method used is also known as Convolutional Neural Networks with Support Vector Machine. This method effectively determines features for the further comparison and detection of objects. (3) Results: The innovative model for generating a workplace procedure, using CNN-SVM architecture, once built, can then be used to provide a learning process to the employees of manufacturing companies. The novelty of the proposed methodology is its architecture, which combines the acquisition of specialist knowledge and formalising and recording it in a useful form for new employees in the company. Moreover, three new algorithms were created: an algorithm to match features, an algorithm to detect each activity in the workplace procedure, and an algorithm to generate an activity scenario. (4) Conclusions: The efficiency of the proposed methodology can be demonstrated on a dataset comprising a collection of workplace procedures, such as the repair of the solid fuel boiler. We also highlighted the impracticality for managers of manufacturing companies to support learning processes in a company, resulting from a lack of resources to teach new employees.

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“…Bayesian method [38] is a method used to solve statistical problems based on Bayesian theory. It can well connect the prior probability and posterior probability of an event.…”

Section: Violation Information Recognition Onmentioning

confidence: 99%

A Violation Information Recognition Method of Live‐Broadcasting Platform Based on Machine Learning Technology

Shen

Yuan

2021

Wireless Communications and Mobile Computing

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With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.

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A model-free Bayesian classifier

Cited by 24 publications

References 35 publications

Representation Learning of Knowledge Graphs With Entity Attributes

Representation Learning of Knowledge Graphs With Entity Attributes

A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

A Violation Information Recognition Method of Live‐Broadcasting Platform Based on Machine Learning Technology

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