The weights initialization methodology of unsupervised neural networks to improve clustering stability

Park, Seongchul; Seo, Sanghyun; Jeong, Changhoon; Kim, Jun Tae

doi:10.1007/s11227-019-02940-4

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…This module also implements two more smoothing functions: Exponentially Weighted Moving Average (EWMA) and Harmonic [19]. b) Weight Selection Module: As shown in previous studies [11], [26], the SOM model used in bitrate selection is sensitive to the initial weight values assigned to various SOM features. Assigning these weights randomly or tuning them manually may reduce the bitrate selection performance.…”

Section: B Lol + Designmentioning

confidence: 99%

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Bentaleb

Akcay

Lim

et al. 2022

IEEE Trans. Multimedia

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Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hardcoded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL's wide deployment. Second, LoL's objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL + , which has been integrated to the official dash.js player in v3.2.0.

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Section: B Lol + Designmentioning

confidence: 99%

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Bentaleb

Akcay

Lim

et al. 2022

IEEE Trans. Multimedia

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“…BP algorithm is a supervised learning algorithm. The idea of BP algorithm is that initial weights and thresholds are first provided to the network; an actual output value of the network is calculated from the input value [10,11]; then, the desired output value is compared with the actual output value; such learning is repeated on the training samples based on the obtained weights and thresholds of the error correction network, and finally, the error between the actual output and the desired output is minimized.…”

Section: Adaptive Genetic Neural Network Algorithmmentioning

confidence: 99%

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

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The weights initialization methodology of unsupervised neural networks to improve clustering stability

Cited by 2 publications

References 12 publications

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

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